Athos

Unlocking the key to the perfect squat

At Athos, we’re always striving to gain deeper insights into the human body. To do that Athos partnered with ProActive, an elite sports performance institute that trains some of the top athletes in the world. We threw the ... Read More

Athos Science: Stronger or Weaker?

Quantifying muscle activity is different than measuring strength. A muscle with higher muscle activation does not necess... Read More

Glute to Hamstring Ratio by Kristen Larsen

Some of the best athletes in the world who move the quickest, jump the highest, and beat out their competition, are not ... Read More

Eccentric Impact on Training Load

One of the questions we often get from coaches is, “Why do eccentric contractions require less muscular activation?” The... Read More

Athos Science: Complete Picture of an Athlete

By Daniel Feeney

By Daniel Feeney Ph.D. Dan has an undergraduate and masters degree in Biomechanics and a Ph.D. in Neurophysiology from the University of Colorado Boulder. Over his academic career Dan has published 14 papers. Dan also stays very active in the American Society of Biomechanics. <h2 id="learn-more-about-dan-s-work-here-"><a href="https://www.researchgate.net/profile/Daniel_Feeney">Learn more about Dan's work here </a></h2> When he’s not working on academic papers, Dan is an accomplished triathlete with multiple national top 20 finishes. Dan is also a research scientist with Athos. He designs experiments and studies to better understand how our sEMG and acceleration signals can be leveraged to help coaches improve training for their athletes. <h2 id="athos-science-complete-picture-of-an-athlete">Athos Science: Complete Picture of an Athlete</h2> Athos provides unparalleled resolution in quantifying the stress placed upon an athlete’s body. Coaches of most sports have relied on accelerometer, heart rate, or ratings of perceived exertion (RPE) data to quantify how hard their athletes are working. In contrast, a revolution in cycling technology in the early 90's involved quantifying power- the amount of work/second- their athletes were doing at each time point. Using this methodology, coaches can accurately quantify how much external work their athletes are doing and use this to determine when to rest an athlete or when to push them further. When combined with a measure of internal stress such as RPE, the coach can begin to understand when an athlete may be accumulating too much or too little of this stress. To date, power is ubiquitous among professionals and amateur cyclists interested in training properly. Much like the revolutionary power meter, Athos sEMG technology can be used to quantify how difficult a practice was. We compared our metric of training load to the gold standard: the work quantified from a power meter. <img src="https://athos-website.s3.amazonaws.com/v2/content/Internal_External_Bike_.jpg" alt="alt alt alt"> There is a strong relationship between work (how hard the effort was) with the training load required to perform this. Given this relationship, we can easily understand if an athlete is fatigued- they will require more internal training load (based on EMG) to accomplish the same external task (power, in this case). To date, there is no method to accurately quantify internal and external stress that works in a team sports environment. While other technologies aim to provide this information, GPS data alone can dramatically underestimate the difficulty of an activity such as change of direction sprints. <img src="https://athos-website.s3.amazonaws.com/v2/content/GPSvsSEMG.jpg" alt="alt alt alt"> Moreover, accelerometer or GPS data will underestimate the stress experienced by an athlete as not all stress is due to movement alone. This is highlighted when comparing positions such as point guard to a forward, who will experience more internal load when rebounding or playing defense in the post, which is not always captured by accelerations. <img src="https://athos-website.s3.amazonaws.com/v2/content/ExternalVInternal.jpg" alt="alt alt alt"> Our training load corresponds well with how hard an athlete is working over the session. In this way, training load per second may be used analogously to power for cyclists. For example, the figure below shows a synchronous change in training load/s with increased velocity. <img src="https://athos-website.s3.amazonaws.com/v2/content/CP.png" alt="alt alt alt"> Training load is in orange and velocity of the athlete is in blue. The athlete began with a warm-up, then had three relatively long efforts followed by four short bursts of speed. In this example, the session was well programmed for the athlete as we saw no decoupling between training load and speed. In the figure below, the athlete performed another running workout, where they had to maintain the same speed for 7 x 400m repeats. The overall trainingload increases for each interval, which shows the internal effort increasing to maintain their goal. <img src="https://athos-website.s3.amazonaws.com/v2/content/Trainingload_fig.png" alt="alt alt alt"> <img src="https://athos-website.s3.amazonaws.com/v2/content/by_muscle.png" alt="alt alt alt"> In this figure, the same athlete was asked to perform seven 400 meter repeats at the same time. Each successive 400 required more load than the previous. This was accompanied by an increase in fatigue (reported by an increase in RPE). Moreover, there was an increase in hamstring activation and a decrease in glute contribution. This is less optimal movement strategy as the glutes are generally stronger than the hamstrings and the glutes only cross the hip while biceps femoris crosses both the hip and knee. In addition to load monitoring, Athos provides unparalleled insight into how each muscle group is being stressed. For example, an important metric that helps prevent injury (Ireland et al., 2003) is the <a href="https://www.liveathos.com/brand/stories/athos-science-importance-of-glute-contribution-by-daniel-feeney">glute/hamstring ratio</a>. <img src="https://athos-website.s3.amazonaws.com/v2/content/Muscular_Activation_Patterns.jpg" alt="alt alt alt"> Using Athos, coaches do not have to sacrifice other metrics such as accelerometer or heart rate data, since we have incorporated those metrics into our product as well. <h2 id="if-you-d-like-to-learn-more-about-athos-please-click-here-"><a href="http://resource.liveathos.com/monitor-glute-to-hamstring-ratio-to-support-healthy-athletes">IF YOU'D LIKE TO LEARN MORE ABOUT ATHOS PLEASE CLICK HERE </a></h2>

Athos Science: Muscle Electromyography

by Daniel Feeney

By Daniel Feeney Ph.D. Dan has an undergraduate and masters degree in Biomechanics and a Ph.D. in Neurophysiology from the University of Colorado Boulder. Over his academic career Dan has published 14 papers. Dan also stays very active in the American Society of Biomechanics. <h2 id="learn-more-about-dan-s-work-here"><a href="https://www.researchgate.net/profile/Daniel_Feeney">Learn more about Dan's work here</a></h2> When he’s not working on academic papers, Dan is an accomplished triathlete with multiple national top 20 finishes. Dan is also a research scientist with Athos. He designs experiments and studies to better understand how our sEMG and acceleration signals can be leveraged to help coaches improve training for their athletes. <h2 id="how-we-produce-force">How We Produce Force</h2> Anytime humans want to move, our brain sends an electrical current to the motor neurons in the ventral horn of the spinal cord which has an axon that projects to the muscles. The muscles in turn must pull on the tendons or bony landmarks to which they are attached to actually generate the movement. If enough current is supplied to a neuron in the spinal cord, an action potential will propagate down the axon (which runs along a peripheral nerve), and to the muscle where a finite number of muscle fibers will contract. There are between 100-4000 motor units that innervate all the fibers in a given muscle and this differs based on the function and location of the muscle. <img src="https://athos-website.s3.amazonaws.com/v2/content/Screen%20Shot%202018-03-12%20at%203.59.10%20PM.png" alt="alt alt alt"> Figure 1. Schematic of a motor unit. The motor neuron is in the ventral horn of the spinal cord where its dendrites act as information receptors. If enough electricity is provided to the motor neuron, an action potential will propagate down the myelinated axon to the muscle and innervate a finite number of muscle fibers (4 in this simplified example). In order to increase our force, our motor neurons will discharge action potentials more rapidly (this is referred to as rate coding) or additional motor units will be activated by the nervous system. Motor units are recruited in a fixed order (Henneman et al., 1954) from smallest to largest due to the progressive increase in input conductance as the size of the neuron increases. Larger neurons also innervate a greater number of muscle fibers, so later recruited units can produce more force. This is a beneficial for human force control. We have all tried to pick up something that looks heavy but is actually light and we lift it too rapidly. In this same way, one does not recruit many fibers for a low level force contraction such as lifting a coffee cup (or risk spilling). A typical discharge rate for motor units is between 10 and 60 pulses per second. <h2 id="if-you-d-like-to-learn-more-about-athos-please-click-here"><a href="http://resource.liveathos.com/semg-for-sports-performance-and-player-readiness">IF YOU'D LIKE TO LEARN MORE ABOUT ATHOS PLEASE CLICK HERE</a></h2> Each motor unit action potential has a distinct shape (amplitude and number of zero crossings) relative to a recording electrode. A schematic overview of this process can be seen in figure 2. There are two separate tasks (A) shows a steady force output, while (B) shows a linearly increasing force. The control scheme from the motor units is displayed: in panel A, a motor unit is discharging action potentials at regular intervals. In panel B, a second unit is recruited as force increases (and the first units discharge rate increases). The algebraic sum of these motor unit action potentials results in the surface EMG signal. <img src="https://athos-website.s3.amazonaws.com/v2/content/Screen%20Shot%202018-03-12%20at%203.59.20%20PM.png" alt="alt alt alt"> Figure 2. Schematic overview of motor unit recruitment. There are two tasks: A. requires a subject to maintain a steady force output. There is a motor unit discharging an action potential at regular intervals during this time. B. The second (right), is a linearly increasing force output. An additional motor unit is recruited while the first unit increases its discharge rate. C. Shows the motor unit action potential shape of both units and their sum, which contributes to the surface EMG. Starting in the late 1700s, it became apparent that an electrical current was required to perform a muscular contraction. Most people credit Etienne Marey with coining the term electromyography (EMG) around 1876. In 1929, Adrian and Bronk created a concentric needle electrode that could be inserted into a muscle to could record action potentials from single motor units in human subjects. Various advances of this technique were developed over the years, and John Basmajian published a book in the 1960s, Muscles Alive, which formed a collated resource for those interested in EMG. Many iterations of EMG systems have come about including high-density and intramuscular EMGs, but Athos is one of very few that embed the sensors into wearable clothing. <img src="https://athos-website.s3.amazonaws.com/v2/content/BrainImage_v1.jpg" alt="alt alt alt"> Figure 3. Overview of force production. A motor neuron in the ventral horn of the spinal cord receives enough current to discharge an action potential causing a muscular contraction. Athos measures the summation of these action potentials at the muscle. This signal provides insight into the amount of time a muscle is active and the intensity of its activation. Surface electromyography records the electrical activity due to the discharging of action potentials from motor units as shown in figure 2. A bipolar electrode is placed on the skin above a muscle belly and can detect electrical activity a given distance from the origin. The output is the surface electromyogram (sEMG), and represents the algebraic sum of action potentials (Day and Hullinger, 2001; Keenan et al., 2005). There is a small delay between the onset of EMG activity and force production, however during sustained contractions there cannot be force without EMG activity. A main challenge of the EMG signal is its interpretation: what can we use it for? <h2 id="what-athos-provides-with-this-signal-">What Athos provides with this signal:</h2> Because there is a strong (albeit nonlinear) relationship between sEMG and force production by a muscle, Athos provides an easy and versatile method to quantify the amount of electrical force a produced by the muscle and this can be used in a variety of ways to better understand the impact and outcomes of athletic training. To date, there is no other system with the ability to understand muscles in the weight room, the training room and in sports-specific practices. Using this muscular activity, Athos can provide insight into imbalances across the body and ratios between the EMG signal produced by each muscle. All of this helps both coaches and athletes understand the physical impact of their training. This enables coaches to manage the accumulation of load on the body, understand injury risk and know where to focus any recovery. If you’d like to learn more about how to use Athos to train your athletes <a href="https://www.liveathos.com/product-facility">please click here</a>.

Validity and Reliability

Athos Staff

Journal of Sports Science and Medicine (2018) 17, 205 - 215 <h2 id="validity-and-reliability-of-surface-electromyography-measurements-from-a-wearable-athlete-performance-system"><a href="https://www.jssm.org/mob/mobresearchjssm-17-205.xml.xml">VALIDITY AND RELIABILITY OF SURFACE ELECTROMYOGRAPHY MEASUREMENTS FROM A WEARABLE ATHLETE PERFORMANCE SYSTEM</a></h2> Scott K. Lynn1,, Casey M. Watkins2, Megan A. Wong3, Katherine Balfany1, Daniel F. Feeney4 <h2 id="abstract">ABSTRACT</h2> The Athos ® wearable system integrates surface electromyography (sEMG ) electrodes into the construction of compression athletic apparel. The Athos system reduces the complexity and increases the portability of collecting EMG data and provides processed data to the end user. The objective of the study was to determine the reliability and validity of Athos as compared with a research grade sEMG system. Twelve healthy subjects performed 7 trials on separate days (1 baseline trial and 6 repeated trials). In each trial subjects wore the wearable sEMG system and had a research grade sEMG system’s electrodes placed just distal on the same muscle, as close as possible to the wearable system’s electrodes. The muscles tested were the vastus lateralis (VL), vastus medialis (VM), and biceps femoris (BF). All testing was done on an isokinetic dynamometer. Baseline testing involved performing isometric 1 repetition maximum tests for the knee extensors and flexors and three repetitions of concentric-concentric knee flexion and extension at MVC for each testing speed: 60, 180, and 300 deg/sec. Repeated trials 2-7 each comprised 9 sets where each set included three repetitions of concentric-concentric knee flexion-extension. Each repeated trial (2-7) comprised one set at each speed and percent MVC (50%, 75%, 100%) combination. The wearable system and research grade sEMG data were processed using the same methods and aligned in time. The amplitude metrics calculated from the sEMG for each repetition were the peak amplitude, sum of the linear envelope, and 95th percentile. Validity results comprise two main findings. First, there is not a significant effect of system (Athos or research grade system) on the repetition amplitude metrics (95%, peak, or sum). Second, the relationship between torque and sEMG is not significantly different between Athos and the research grade system. For reliability testing, the variation across trials and averaged across speeds was 0.8%, 7.3%, and 0.2% higher for Athos from BF, VL and VM, respectively. Also, using the standard deviation of the MVC normalized repetition amplitude, the research grade system showed 10.7% variability while Athos showed 12%. The wearable technology (Athos) provides sEMG measures that are consistent with controlled, research grade technologies and data collection procedures. Key Points Surface EMG embedded into athletic garments (Athos) had similar validity and reliability when compared with a research grade system There was no difference in the torque-EMG relationship between the two systems No statistically significant difference in reliability across 6 trials between the two systems The validity and reliability of Athos demonstrates the potential for sEMG to be applied in dynamic rehabilitation and sports settings <h2 id="introduction">INTRODUCTION</h2> Surface electromyography (sEMG) provides access to the activation signal that causes the muscle to generate force, produce movement, and accomplish the essential functions of everyday life (DeLuca, 1997). The sEMG signal represents the sum of the motor unit action potentials recorded by the electrodes and provides crucial insight into the nervous system’s activation of the muscle (Day and Hullinger, 2001; Keenan et al., 2005). sEMG is used in various applications including clinical, research and sport to explore the neuromuscular system and the relationship between muscle activation, movement and force. For example, sEMG provides clinicians with a robust biofeedback tool that has been demonstrated to improve muscle function in children with cerebral palsy (Bloom, 2010). Moreover, sEMG has been effective in diagnosing, treating and researching populations with various pathologies including hypo- and hypertonicity (Herrington, 1996); stroke (Park and Kim, 2017); lower back pain (Kaur and Kumar, 2016; Matheve et al., 2017); and patellofemoral pain syndrome (PFPS) (Kalytczak, 2016). The research applications of sEMG are also broad and include detecting differences in muscle activation patterns with changes in exercise or movement technique (Lynn and Noffal, 2012; Lynn and Costigan, 2009), recognizing abnormal activation strategies (Michener et al., 2016), developing methods for prosthetic control (Daley et al, 2012), as well as developing biomechanical models to predict the loading on joints (Callaghan et al., 1998). Movement strategy is critical in sport and sEMG has been used to evaluate muscle activation in sport applications including recovery, performance and evaluating injury risk factors. As a biofeedback tool, sEMG has been demonstrated to increase quadriceps strength recovery post anterior cruciate ligament (ACL) reconstruction (Draper, 1990). Further, muscle activation based on sEMG has been used to evaluate the efficacy of different training techniques such as comparing the activation from different muscle groups based on exercise or equipment type (Krause, 2009) or evaluating the impact of training technique on specific physiological adaptation (Walker, 2012). Muscle activation data has also been used to research criteria that may relate to different injury risk in sport, for example, quadriceps dominance during single leg squats as a possible risk indicator of ACL injury (Zeller, 2003). Although the clinical, research and sport applications of sEMG are extensive, there are many hurdles that make it difficult for wide ranging use. Measurement of sEMG typically requires significant setup cost including skin preparation and application of single use adhesive-based Ag/AgCl electrodes (SENIAM) (Merletti, 1997). Also, electrodes are generally tethered to a data acquisition system constraining the movement of the subject and context that can be studied. Further, the signal acquired often requires further processing and filtering by the user to report on metrics based on the data. The setup cost and complexity of the equipment as well as the extensive processing often required makes sEMG analysis and application difficult outside the laboratory or clinic. With advancements including component miniaturization, material development and improved manufacturing methods, new technologies for measuring human physiology are emerging that may reduce the setup cost and complexity of measuring sEMG. The Athos® training system (<a href="http://www.liveathos.com">www.liveathos.com</a>) is an example of one of these new technologies. Athos has integrated sEMG measurement into the construction of athletic compression apparel. The sEMG signals are acquired by a portable device that clips into the apparel, processes, and sends wirelessly to a client device for presentation to the coach or athlete. Through the combination of a mobile and browser application, Athos provides athletic trainers, coaches and athletes with performance metrics derived from the sEMG measurements. The sEMG based metrics are used to evaluate activation and recruitment patterns between muscles and over time during training. While Athos provides sEMG measurements integrated into the construction of compression athletic apparel, the validity and reliability of this system needs further testing. One study has compared the Athos sEMG signal to a research grade system (Aquino & Roper, 2018) and found it to be valid; however, the two sEMG systems were not worn concurrently, so data from the same contractions could not be compared. Therefore, the purpose of this study is to compare Athos sEMG measurements against an established research system and protocol (Finni et al., 2007) on the same contractions. Athos electrodes are integrated into the construction of the garment. The research system comprises traditional Ag/AgCl adhesive electrodes placed directly distal the Athos electrodes and following standard SENIAM protocol for skin preparation. There was no difference in filtering applied prior to sampling across the EMG spectrum of 10-500 Hz and the sampled signals were processed using the same processing steps. The validity of the Athos system was evaluated by first comparing characteristics of the sEMG signal from both systems and second by comparing the relationship between sEMG from both systems and the resulting torque produced by those contractions. We evaluated the reliability of sEMG measures from the two systems across days where the electrodes are re-applied. We hypothesized that there would be no significant differences in sEMG output or the relation between EMG and torque for the two systems. Moreover, we hypothesized that the test-retest reliability of the sEMG signal from Athos would be comparable to the research grade system. <h2 id="methods">METHODS</h2> Subjects Twelve healthy subjects (6 males, 6 females, see <a href="https://www.jssm.org/mob/ShowTable.php?jid=jssm-17-205.xml&TableId=table001">Table 1</a>) were recruited for this study. Subjects were screened through a pre-research questionnaire to determine level of training and ensure full commitment to the completion of data collection. Level of training was defined as untrained (< 1 year training; 1 male, 3 female), recreationally trained (1-3 years training; 3 male, 3 female) and expertly trained (> 3 years training; 1 male, 1 female). Testing was performed at the same time for each testing trial, and subsequent trials were separated by a minimum of 48 hours. Each subject was required to participate in a total of seven testing trials over a three-week period. All subjects were notified of potential risks and provided written informed consent approved by the University Institutional Review Board prior to data collection. Set-up For each subject, anthropometrics (hip and waist measurements) were recorded to determine the appropriate Athos gear size. Each subject used the same gear throughout the whole study, and gear was washed following the last trial of each week. SEMG measurements from the vastus lateralis, vastus medialis and bicep femoris were collected with both Athos and the Biopac electrodes (Biopac Systems, Inc., Goleta, California) simultaneously. The Athos compression garments were fit to each subject to ensure the electrodes embedded in the garments were directly over the muscle bellies of vastus lateralis, vastus medialis, and biceps femoris. Athos electrodes are designed to provide a bipolar differential EMG measurement with an interelectrode distance of 2.1 cm (<a href="https://www.jssm.org/mob/ShowFigure.php?jid=jssm-17-205.xml&FigureId=fig001">Figure 1</a>). Athos electrodes are comprised of a conductive polymer and no skin or electrode preparation was performed at the site corresponding to each electrode. No skin or electrode preparation was performed at the site corresponding to each Athos electrode as in a practical setting, skin preparation is not performed when wearing Athos. For each muscle, the Athos shorts were cut just below the Athos bipolar electrodes to place the Biopac bipolar electrodes (Biopac EL500, Ag/AgCl electrodes, Bio-Pac systems Inc., Goleta, CA, USA) as close to the Athos electrodes as possible and directly distal on the same muscle. The bipolar Biopac electrodes provided a differential EMG measurement and an interelectrode distance of 2.1 cm was used to match the interelectrode distance of the Athos electrodes. When applying Biopac electrodes, the area of skin was shaved and cleaned with an alcohol wipe. Biopac electrodes were marked on the skin and the electrode location was re-marked following testing to prevent fading and keep the placement consistent for each trial. The Biopac reference electrode was placed on the right wrist at the styloid process of the ulna as has been done previously (Cochrane et al., 2014). Experimental procedure The study protocol consisted of 1 baseline testing session and 6 repeated testing sessions (<a href="https://www.jssm.org/mob/ShowFigure.php?jid=jssm-17-205.xml&FigureId=fig002">Figure 2</a>). A HUMAC Norm (CSMi, Inc., Stoughton, MA, USA) isokinetic dynamometer was used to control the knee extension and flexion sets and to measure angular displacement and torque output. The dynamometer was used to reduce variability in the performance of the movement by controlling for speed and movement position. Torque output measurements were taken to control for repeatable torque across trials and to relate the output torque to the resulting sEMG response for each muscle. Day 1: Familiarization and Baseline Testing: Prior to the first data collection trial, height and mass were recorded. Subjects were instructed to cycle for 10-minutes on a stationary bike at a self-selected pace followed by a dynamic warm-up. Subjects were then seated on the HUMAC Norm dynamometer and were positioned according to the HUMAC testing and rehabilitation user’s manual with the padded arm of the dynamometer positioned 3 cm proximally to the lateral malleolus and the axis of rotation of the knee aligned with the axis of rotation of the dynamometer. Isometric 1 repetition maximum (RM) strength testing for knee extensors and knee flexors was performed with the knee positioned at 90° of flexion and the hip at 85° as was previously described (Luc et al., 2016; Roberts et al., 2012). All tests included familiarization comprising warm-up repetitions to become familiar with each speed and movement. The isometric protocol to determine each subject’s 1RM consisted of 5 second isometric contractions intermittent with 5 seconds of rest at each intensity, starting at 50 percent MVC for 5 repetitions, 70 percent MVC for 3 repetitions, 90 percent MVC for 1 repetition, and 100 percent for 1 repetition. A 1-minute rest followed each effort set. Following isometric testing, subjects performed three repetitions of concentric-concentric knee extension and flexion at 100 percent MVC for each testing speed: 60, 180, and 300 deg/sec. Each of these repetitions involved moving the knee from 90° of flexion to 0° of knee flexion, or where the knee is fully extended and back to 90° of flexion. The peak torque achieved by the subject during each set was recorded and used to establish a +/-10% torque window for each speed and percent MVC for the following 6 trials of the study. Any subsequent trials which produced torque values outside of this range were not counted and repeated. Days 2-7: Subjects were asked to attempt to maintain consistent patterns of sleep, nutrition, and activity between testing days. Prior to each trial, subjects completed a daily questionnaire consisting of sleep, nutrition, and activity information in order to ensure there were no large differences in these factors that could alter performance. Participants performed the standardized cycling and warm up protocol. Each trial consisted of 9 sets (<a href="https://www.jssm.org/mob/ShowFigure.php?jid=jssm-17-205.xml&FigureId=fig002">Figure 2</a>) with each set consisting of 3 knee extension and flexion repetitions. The 9 sets included 1 set per speed (at 60 deg/s, 180deg/s, and 300 deg/s) and MVC level (50 percent, 75 percent, and 100 percent) combination. Concentric torque, position, velocity and sEMG data were collected during each set. Effort levels were monitored based on the 1RM peak torque established during day 1 baseline testing for each subject, speed and MVC combination. The research administrator examined data after each set to determine if the effort level achieved matched the baseline torque outputs (within +/- 10%). If torque output was outside the approved range, the participant was required to attempt testing at that speed-effort pairing again and no more than 3 attempts were made before moving to the next pairing. The order of 9 sets was randomized between participants, but each participant performed the same order for all six testing sessions. <h2 id="signal-acquisition-and-processing">Signal acquisition and processing</h2> Athos provided sampled sEMG data at 1kHz, no gain was applied to the analog signal and only an anti-aliasing filter was applied prior to sampling. The anti-aliasing filter prevents high frequency noise greater than 500Hz from aliasing into the sEMG spectrum. Since the sEMG spectrum generally does not extend beyond 500 Hz the anti-aliasing filter will have negligible influence on the sEMG signal. Biopac data was sampled at 1024 Hz, the analog signal was amplified by a factor of 1000 and a bandpass filter with cutoff frequencies at 10 Hz and 500 Hz were applied prior to sampling (EMG100C; BIOPAC Systems Inc., Goleta, CA, USA; bandwidth = 10–500 Hz). After Athos and Biopac signals were sampled and aligned to 1kHz, both were processed with the same set of filtering steps to ensure an equivalent spectrum of the signal from each system and to produce an envelope representing the sEMG signal power. Filtering included a linear bandpass filter with center frequency at 120 Hz, linear notch filter at 60 Hz, rectification and linear envelope. The linear envelope was then downsampled by a factor of 25 and further smoothed using a 16 sample root mean square (RMS). The processing steps described above are supported as a method of calculating an amplitude representation of the sEMG signal and described in ‘Guidelines for Reporting SEMG Data’ (Merletti, 1997). The final result is an RMS sEMG from both systems at the same sampling rate. This is required to calculate reliability and validity. Athos data includes a measure of contact quality, which is estimated from the amplitude of a high frequency signal outside of the sEMG frequency spectrum. This signal was evaluated to determine the quality of contact of each of the Athos electrodes for each trial. Each set of data comprised knee extension and flexion repetitions at a given MVC level. If the amplitude of the high-frequency contact signal exceeded a given threshold for over 10% of the set, that set was determined to be poor contact quality. In total 18% of the sets were determined to have poor contact quality and were not included in further analysis. The Biopac sEMG data and HUMAC dynamometer data was collected with the same software (AcKnowledge, v.3.8.1, Biopac Systems Inc.) and were therefore aligned in time and at the same sampling rate of 1024 Hz. To align the Athos data to the Biopac and dynamometer data we compared the standard deviation from a 200 ms sliding window to the standard deviation of the resting noise (Dideriksen et al., 2017). The standard deviation of the resting noise was taken from the first second of each set during which the subject was stationary. The onset event of the first repetition was determined for the Athos system as the point where the sEMG standard deviation was 10x the magnitude of the standard deviation of the resting noise. The Athos onset event was then aligned to the moment where the dynamometer arm started to move. This produced the best alignment of the data from both systems. It is well established that sEMG activity precedes mechanical output or motion in the range of 50 ms (DeLuca, 1997), but we found this difference had negligible impact on alignment for the purpose of this study. After alignment a plot was generated for each set to visually evaluate the resulting Athos and Biopac alignment as well as to check for any other test issues. An example plot is shown in <a href="https://www.jssm.org/mob/ShowFigure.php?jid=jssm-17-205.xml&FigureId=fig003">Figure 3</a>, the processed envelope for both Athos (black) and Biopac (grey) are overlaid after the alignment has been corrected based on the above described method. For this example, it is possible that the Biopac electrode contact quality was lower than that of the Athos electrode for the bicep femoris muscle group. This difference in contact quality could explain the increase in baseline noise and lower signal amplitude measured from Biopac as compared to Athos for this set. and may be due to the fact the subjects were seated and there may have been some pressure on the hamstring electrodes. During visual inspection of each trial, 22% of the sets were removed from further analysis due to either incorrect alignment or errors in the testing methodology. Incorrect alignments were mostly due to trials where the subject was not fully relaxed when the data collection began, this resulted in large resting noise. Error conditions included recordings with less than 3 measured repetitions, cases where the subject was unable to achieve the desired speed or produced inconsistent speed across repetitions. After the datasets were aligned, parameters were calculated for each repetition based on the processed RMS of the sEMG signal. First the three repetitions of each set were segmented for the sEMG and torque time series data using the zero crossings of the dynamometer arm velocity. For each segmented repetition parameters were calculated as dependent variables for the processed RMS waveform of the sEMG signal including the 95th percentile magnitude, peak magnitude, and sum of the total sEMG over the repetition. The same parameters were also calculated for torque over each repetition. The 95th percentile and peak magnitude both represent a peak amplitude parameter taken from the processed sEMG waveform over each repetition with the 95th percentile magnitude more resilient to large magnitude sample outliers during the repetition. The sum represents the accumulation of the sEMG signal over the repetitions. The 95th percentile, peak and sum dependent variables for both sEMG and torque across all repetitions, sets and subjects were then used to evaluate the validity and reliability of the new wearable system (Athos) as compared to the gold standard research grade sEMG system (Biopac). <h2 id="data-analysis">Data analysis</h2> We evaluated two measures of validity between Athos and Biopac. First, we compared the characteristics of the RMS sEMG signal for each muscle, speed, and percent MVC between the two systems. Secondly, we compared the strength and directionality of the relationship between sEMG metrics and torque output between Athos and Biopac. To evaluate differences between sEMG metrics obtained from Athos and Biopac, we used a linear mixed model to evaluate if there was a significant effect of system (Athos or Biopac), session (2-7), speed, or percent MVC on each dependent variable extracted from the sEMG waveforms collected. We used post-hoc Bonferroni adjusted p-values for pairwise comparisons. This model was estimated separately for the three-dependent variables: 95th percentile, peak, and sum of each repetition within a set. The combination of the two quad muscles measured, vastus lateralis and vastus medialis, were summed as an additional muscle grouping for comparison. We evaluated differences in sEMG characteristics (95%, peak, and sum) by creating a linear mixed model ANOVA with subject, speed, and muscle as independent variables and sEMG metric (95%, peak, or sum) as the dependent variables between Athos and Biopac. The linear model was calculated using R (R core team) using the lme4 package (Bates et al., 2015). To assess the strength of the relationship between torque and EMG for both systems, we fit subject specific regressions of sEMG and torque output for each muscle and speed combination that spanned 50, 75 and 100% MVC torque. These all ended up producing linear relationships. <a href="https://www.jssm.org/mob/ShowFigure.php?jid=jssm-17-205.xml&FigureId=fig004">Figure 4</a> shows an example for one subject and represents all extension repetitions spanning all MVC levels at 180 deg/s. Each point represents the 95th percentile sEMG dependent variable from vastus lateralis against the torque generated during a knee extension repetition. The EMG values were normalized to the maximal voluntary contraction at each speed for each subject. We examined differences in the relationship between torque and EMG by comparing the coefficient of determination between systems using a Wilcoxon-Rank_Sum Test due to non-normally distributed data. To assess reliability first the repetitions were constrained to within +/-10% of the mode torque for each subject, speed and effort combination. This was necessary to ensure day-to-day variations in EMG amplitude were not due to differences in torque output. During the test protocol a range of effort levels were measured by asking the subjects to perform the movement at 50%, 75% and 100% MVC torque. The reliability of the EMG metrics was then accessed by calculating the variation in repetition amplitude in two ways, first as the coefficient of variation (standard deviation divided by the mean), and second as the standard deviation of the normalized repetition amplitude. Metrics based on sEMG amplitude are often normalized and presented as a relative measure against a baseline, such as one repetition maximum (Merletti et al., 1997; Farina et al., 2014). This allows the sEMG metric to be presented as a percentage of baseline contraction. The second approach provides a measure of variability as a percentage of MVC amplitude. The reliability measures were calculated using the 95th percentile repetition amplitude per muscle group for both Athos and Biopac. Reliability was also calculated for the sum of vastus lateralis and vastus medlias muscle groups. <h2 id="results">RESULTS</h2> Validity The validity results comprise two main findings. First, there is not a significant main effect of system (Athos or Biopac) on sEMG characteristic (95%, peak, or sum) and the relationship between torque and EMG is not significantly different between Athos and Biopac. A 2-way mixed model ANOVA indicated significant main effects of speed (χ2 = 10.02, p = 0.005), but not of system (Athos or Biopac) (χ 2 = 0.65, p = 0.42) on sEMG amplitude. To be conservative, we performed post-hoc paired t-tests for each speed, muscle, and percent MVC combination and presented all significant differences in<a href="https://www.jssm.org/mob/ShowFigure.php?jid=jssm-17-205.xml&FigureId=fig005"> Figure 5</a>. There was no significant difference for 95th percentile, peak, or sum sEMG metrics between Athos and Biopac (Bonferroni adjusted p > 0.001) for any speeds or muscles. A model between torque and sEMG was calculated between all sEMG metrics (95%, peak, sum), muscles and speeds separately and was statistically significant suggesting a significant linear relationship in our data set between torque and sEMG. The coefficient of determination ranged from 0.15 to 0.67 for all subjects. Critically, there was no significant difference in the strength of the relationship between systems (Wilcoxon Signed Rank p-values shown): 95% (BF: p = 0.41, VL: p = 0.45, VM: p = 0.63, VL+VM: p = 0.91), peak (BF: p = 0.42, VL: p = 0.22, VM: p =0.29, VL+VM: p = 0.56), and sum (BF: p = 0.64, VL: p = 0.21, VM: p = 0.29,VL+VM: = 0.09). <a href="https://www.jssm.org/mob/ShowTable.php?jid=jssm-17-205.xml&TableId=table002">Table 2</a> and <a href="https://www.jssm.org/mob/ShowTable.php?jid=jssm-17-205.xml&TableId=table003">Table 3</a> compare the strength of correlation between Athos/Biopac and torque. The first table shows the correlation for reps corresponding to 60 deg/s and the second for 300 deg/s. The two controlled speeds were used to represent both controlled strength and explosive power movements experienced in sport. Biopac shows on average a 4% higher correlation with torque. Both systems demonstrate a strong average correlation between sEMG and torque output across the 6 trials. Reliability The coefficient of variation of the 95th repetition amplitude across trials is shown in <a href="https://www.jssm.org/mob/ShowTable.php?jid=jssm-17-205.xml&TableId=table004">Table 4</a> at the 100% MVC level and each speed. The variation averaged across speeds is 0.8%, 7.3% and 0.2% higher for Athos for the bicep femoris, vastus lateralis and vastus medialis respectively. However, when the quads are summed together Athos demonstrates slightly lower variation at 19.9% compared to 20.4% for Biopac. As expected, the variability is higher at the higher speeds and the difference in variability between Biopac and Athos slightly increases at higher speeds. The standard deviation as a percentage of MVC amplitude is shown in <a href="https://www.jssm.org/mob/ShowTable.php?jid=jssm-17-205.xml&TableId=table005">Table 5</a>. Biopac shows on average 10.7% variability and Athos 12% across all speed and MVC levels. Again, when the quads are summed together Athos demonstrates a greater decrease in variability compared to Biopac. The distribution of the variability as a percentage of MVC amplitude is represented with the boxplot in <a href="https://www.jssm.org/mob/ShowFigure.php?jid=jssm-17-205.xml&FigureId=fig006">Figure 6</a>. The boxplot whiskers show the 5th to 95th extents of the distribution. The average variation is represented with the line across each box and the lower and upper limits of the box represents the 25th and 75th percentiles of the variation distribution. <h2 id="discussion">DISCUSSION</h2> We investigated the validity and reliability of the Athos sEMG system to characterize muscle activation patterns during isokinetic knee extension and flexion. We found strong consistency with a standard research grade EMG system (Biopac), a strong relationship between force output and normalized sEMG measurements from both Athos and Biopac, and moderate to high test-retest reliability of the Athos electrodes. Validity To assess validity of Athos compared to Biopac, we investigated differences in sEMG metrics at each speed, muscle, and percent MVC combination. There was no significant difference in signal repetition amplitude (95%, peak, or sum) measured between systems across all muscles measured. Based on a post-hoc power calculation using the standard deviation and mean values for each EMG metric and our sample size, we calculated a minimal detectable difference in EMG output of 0.3 standard deviations from the mean. It is unlikely that small differences (near 0.3 SDs) are significantly meaningful in an athletic setting. Lastly, differences in the alignment of the iliotibial tract and subcutaneous tissue composition may affect the individual quadriceps recording sites, while summing them removes most of this variability in EMG signal content. Therefore, it is remarkable both systems had no significant differences in normalized EMG output for any metric. There was no significant difference in the strength of the relationship between sEMG metrics and torque output between systems. In our data set, both Athos and Biopac sEMG metrics were linearly related to torque output longitudinally across the six trials and days. Correlation coefficients presented in <a href="https://www.jssm.org/mob/ShowTable.php?jid=jssm-17-205.xml&TableId=table003">Table 3</a> and <a href="https://www.jssm.org/mob/ShowTable.php?jid=jssm-17-205.xml&TableId=table004">Table 4</a> demonstrate a similar magnitude and directionality of correlation between sEMG and torque output for both systems, without a significant inter-system difference. The significant linear relationship and correlation coefficients demonstrate the ability for Athos to capture the same relationship between muscle activation and torque output over a range of speeds representing controlled and high velocity movements experienced in sport. Further, even at the highest speed, which represents dynamic movements experienced in sport, the strength of correlation between sEMG and torque was comparable between systems. The Athos electrodes do not use an adhesive to reduce electrode movement and corresponding artifact and yet the strength of correlation is comparable during high velocity movement. The comparable reliability between Athos and Biopac at higher velocities supports the efficacy for Athos to be used to measure dynamic sport movements without sacrificing measurement accuracy compared to a research grade system. It’s important to note that while the strength of correlation is comparable, 18% of sets were removed due to unreliable contact quality from at least one of the muscles measured with Athos. The sets removed primarily occurred at the start of the trial for a given day. One possible explanation is that in these cases the warmup was not sufficient to allow the impedance between the sensors and the skin to decline, thereby improving contact quality. This does emphasize that while Athos demonstrates comparable correlation during high velocity movements, this result was based on good contact quality sets only. A sufficient warmup and settling period may be required before valid and comparable measurements are provided. The relationship between sEMG amplitude and force output is still debated and likely depends on a number of factors including force output level and muscle physiology such as fiber type and size diversity (Alkner et al., 2000; De Luca, 1997; Lawrence and De Luca, 1983; Milner-Brown and Stein, 1975). While the relationship between absolute sEMG and force output is bi-linear between low and high-forces (Day and Hullinger, 2001; Keenan et al., 2005), the normalized sEMG and force relationship is approximately linear across the full range of force output (Fuglevand et al., 1993; Staudenmann et al., 2010). A review by Staudenmann et al. (2010) has concluded that although the relationship between sEMG amplitude and force is not necessarily linear for all muscle groups and applications, linear models are often inevitably used and provide a reasonable description of the relationship. Regardless of the linearity of this relationship through the entire range of muscle forces, there was no significant difference in the strength of this relationship between systems for the torque outputs measured. The results of this study support the conclusions of Staudenmann et al., (2010) for the muscles measured and protocol applied. We only tested from 50-100% MVC, and therefore likely experience amplitude cancellation from the bipolar recordings. Critically, because the EMG-torque relationship is not different between systems, any signal cancellation is similar between systems. Reliability There is not a statistically significant difference in reliability within or among sessions between Athos and Biopac. The coefficient of variation of sEMG amplitude is only 1% higher from Athos for both the bicep femoris and vastus medialis and 7% higher for vastus lateralis. sEMG reliability has been evaluated in previous studies, for example Yang and Winter (1983) evaluated the reliability of triceps sEMG amplitude during isometric contractions at 100%, 50% and 30% MVC across three days. To assess reliability, Yang and Winter (1983) processed the sEMG signal to generate a linear envelope. The amplitude of the linear envelope was compared across sets at each MVC level. The coefficient of variation in EMG amplitude at 100%, 50% and 30% MVC levels between days was 16.4%, 15.2% and 12.0%, respectively, while the variation within days was 9.1%, 8.5% and 10.3% (Yang and Winter, 1983). Results from the present study compare well with reliability reported by Yang and Winter (1983). For example, at 100% MVC and 60 deg/s, the coefficient of variation averaged across muscle groups was 20.1% from Athos and 18.6% from Biopac compared with 16.4% measured by Yang and Winter. The higher variability noted in this current study could be expected as we tested the variability of isokinetic contractions while Yang and Winter (1983) tested the variability of isometric contractions. It’s important to note that variability measured from any sEMG measurement includes measurement error, movement variability, and physiological variability. Measurement error includes variability introduced by the measurement system, such as noise caused by electrode movement during dynamic contractions and differences in electrode positioning, or the fact that the subjects were seated, and the hamstring electrodes may have been compressed between the seat and the leg. Movement variability is introduced by differences in how the subject performs the movement, differences in body position causing differences in muscle recruitment. Physiological variability is introduced by differences in the physiological state of the subject within a trial and between trials. The goal of this study was to examine the differences in measurement errors between the Athos and Biopac system, therefore every effort was made to reduce the movement and physiological variability. The movement variability was reduced by using an isokinetic dynamometer and following strict manufacturer’s recommendations in setting the subject up before every trail. Physiological variability was reduced by testing each subject at the same time on subsequent days, maintaining consistent rest periods between sets and having subjects note their sleep, hydration, nutrition, and exercise between each testing session. The individual components of the variability cannot be separated, but by comparing Athos and Biopac we can interpret differences between the measurement errors of each system and evaluate the performance of Athos as compared to a traditional research grade EMG system. We expect movement and physiological variability to have equivalent impact on Athos and Biopac data and therefore differences in variability should reflect differences in measurement error of the two systems. The small difference in overall measurement variability between Athos and Biopac suggests that Athos does not introduce significant measurement variability despite the form factor of the Athos system. Athos electrodes are built into compression apparel reducing complexity and setup cost by not requiring adhesive electrodes to be re-applied after each trial, careful skin preparation and additional reference electrodes. While Athos EMG measures compare well with those of a research grade EMG system, there is a moderate day-to-day variability inherent to EMG recording that is influenced by the measurement error, movement and physiological variability described above. Even when movement is controlled, as in this study, there may be variability in muscle activation strategies across muscle groups that may influence the variability in amplitude from a specific muscle across trials. For example, in this study at 100% MVC a common activation pattern measured was an increase in left gluteus maximus and bicep femoris activation during right concentric knee extension. One explanation may be that the left gluteus maximus and bicep femoris are activated to generate torque about the hip to support additional force during higher knee extension loads and this may influence the activation and variability measured from the right quads. Further research is needed to understand how these different forms of variability would be represented on athletes outside of the lab and how it would influence comparisons for an athlete across training sessions. From this study it was demonstrated that in a controlled setting Athos has comparable reliability to a research grade system. Based on this result, Athos has the potential to measure the movement and physiological variability outside of the lab without introducing measurement error as compared to a research grade system; although this requires further testing to confirm. The ability to collect valid and reliable sEMG information in any setting can be a valuable tool in understanding how athlete’s movement and physiology is changing across training sessions. This may also have clinical and ergonomic uses in tracking muscle activation patterns in patients and workers during work tasks and activities of daily living. <h2 id="conclusion">CONCLUSION</h2> This study has demonstrated that over a range of dynamic contractions Athos provides measures of sEMG that are consistent with controlled, research grade technologies and techniques. There were no significant differences between normalized EMG amplitude or in the strength of the relationship between sEMG and torque output between Athos and Biopac. Also, no significant differences were seen in variability between Athos and the research grade system. The close comparison demonstrates that Athos does not add significant measurement error that limits application compared to the research grade system. The overall variability measured from both Athos and Biopac contains multiple components. The goal of Athos is to surface the physiological and performance variability down to individual muscles and to do so not just in the lab, but across an athlete’s training in the weight room, training room and on the field, pitch, court or track. Many studies have looked at the efficacy of applying sEMG measurements in sport (Clarys and Cabris, 1993; Draper, 1990; Snarr 2017; Zeller 2003). Further research is needed to study the use of sEMG in different applications and to understand how to interpret the data in less controlled scenarios, outside of the lab. This study has evaluated the Athos system in terms of validity and reliability and has demonstrated the efficacy of Athos as compared to a research grade system to support furthering research and application of sEMG both in and out of the lab. ACKNOWLEDGEMENTS The authors would like to acknowledge all those that participated in the study. Three of the authors (Lynn, Balfany, Feeney) are consultants for the company who make the wearable EMG device (Mad Apparel Inc., dba Athos). The other authors have no conflicts of interest to declare. All experiments comply with the current laws of the country. <h2 id="author-biography">AUTHOR BIOGRAPHY</h2> Scott K. Lynn Employment: Associate Professor – CSU Fullerton Degree: PhD – Queen’s University, Canada Research interests: Golf biomechanics, movement efficiency, rehabilitation/clinical biomechanics, strength & conditioning. E-mail: <a href="mailto:slynn@fullerton.edu">slynn@fullerton.edu</a> Casey M. Watkins Employment: Auckland University of Technology (AUT) – PhD Candidate Degree: MSc – CSU Fullerton Research interests: Strength and Conditioning (speed & power assessments, neurophysiological responses to training interventions) E-mail: <a href="mailto:cwatkins025@gmail.com">cwatkins025@gmail.com</a> Megan A. Wong Employment: PhD Student – Cardiff Metropolitan University Degree: MS – CSU Fullerton Research interests: Injury risk in youth athletes/strength and conditioning E-mail: <a href="mailto:mwong2@cardiffmet.au.uk">mwong2@cardiffmet.au.uk</a> Katherine Balfany Employment: MS Student – CSU Fullerton Degree: BS Exercise & Sport Science University of Wisconsin - La Crosse (UW-La Crosse) Research interests: Exercise physiology and biomechanics in sport performance; sport and exercise psychology E-mail: <a href="mailto:kbalfany@csu.fullerton.edu">kbalfany@csu.fullerton.edu</a> Daniel F. Feeney Employment: Mad Apparel Inc. – Research Scientist Degree: PhD–University of Colorado - Boulder Research interests: Mathematical modeling investigating the link between the central nervous system, motor neuron function, and movement in humans E-mail: <a href="mailto:daniel@liveathos.com">daniel@liveathos.com</a> REFERENCES Alkner B.A., Tesch P.A., Berg H.E. (2000) Quadriceps EMG/force relationship in knee extension and leg press. Medicine and Science in Sports and Exercise 32, 459-463. Aquino J., Roper J.L. (2018) Intraindividual variability and validity in smart apparel muscle activity measurements during exercise in men. International Journal of Exercise Science 11, 516-525. Bates D., Maechler M., Bolker B.M., Walker S.C. (2015) Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1-48. Bloom R., Przekop A., Sanger T.D. (2010) Prolonged electromyogram biofeedback improves upper extremity function in children with cerebral palsy. Journal of Child Neurology 25, 1480-1484. Callaghan J.P., Gunning J.L., McGill S.M. (1998) The relationship between lumbar spine load and muscle activity during extensor exercises. Physical Therapy 78, 8-18. Clarys J.P., Cabri J. (1993) Electromyography and the study of sports movements: a review. Journal of Sports Sciences 11, 379-448. Cochrane K.C., Coburn J.W., Brown L.E., Judelson D.A. (2014) Effects of diverting activity on strength, electromyographic, and mechanomyographic signals. Journal of Strength and Conditioning Research 28, 1203-1211. Daley H., Englehart K., Hargrove L., Kuruganti U. (2012) High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control. Journal of Electromyography and Kinesiology 22, 478-484. Day S.J., Hulliger M. (2001) Experimental simulation of cat electromyogram: evidence for algebraic summation of motor-unit action-potential trains. Journal of Neurophysiology 86, 2144-2158. DeLuca C.J. (1997) The use of surface electromyography in biometrics. Journal of Applied Biomechanics 13, 135-163. Dideriksen J.L., Feeney D.F., Almuklass A.M., Enoka R.M. (2017) Control of force during rapid visuomotor force-matching tasks can be described by discrete time PID control algorithms. Experimental Brain Research 235, 2561-2573. Draper V (1990) Electromyographic biofeedback and recovery of quadriceps femoris muscle function following anterior cruciate ligament reconstruction. Physical Therapy 70, 11-17. Farina D., Merletti R., Enoka R.M. (2014) The extraction of neural strategies from the surface EMG: an update. Journal of Applied Physiology 117, 1215-1230. Finni T., Hu M., Kettunen P., Vilavuo T., Cheng S. (2007) Measurement of EMG activity with textile electrodes embedded into clothing. Physiological Measurement 28, 1405-1419. Fuglevand A.J., Winter D.A., Patla A.E. (1993) Models of recruitment and rate coding organization in motor- unit pools. Journal of Neurophysiology 70, 2470-2486. Herrington L (1996) EMG biofeedback: What can it actually show?. Physiotherapy 82, 581-583. Kalytczak M.M., Garcia Lucareli P.R., do Reis A.C., Bley A.S., Biasotto-Gonzalez D.A., Ferrari Correa J.C., Politti F. (2016) Kinematic and electromyographic analysis in patients with patellofemoral pain syndrome during single leg triple hop test. Gait & Posture 49, 246-251. Kaur G., Kumar P. (2016) Compare the effectiveness of EMG biofeedback assisted core stability exercises versus core stability exercises alone on pain and disability in patients with low back pain. International Journal of Physiotherapy 3, 376-380. Keenan K.G., Farina D., Maluf K.S., Merletti R., Enoka R.M. (2005) Influence of amplitude cancellation on the simulated surface electromyogram. Journal of Applied Physiology 98, 120-131. Krause D.A., Jacobs R.S., Pilger K.E., Sather B.R., Sibunka S.P., Hollman J.H. (2009) Electromyographic analysis of the gluteus medius in five weight-bearing exercises. Journal of Strength and Conditioning Research 23, 2689-2694. Lawrence J.H., De Luca C.J. (1983) Myoelectric signal versus force relationship in different human muscles. Journal of Applied Physiology: Respiratory, Environmental and Exercise Physiology 54, 1653-1659. Luc B.A, Harkey M.H., Arguelles J., Blackburn T., Ryan E.D., Pietrosimone B. (2016) Measuring voluntary quadriceps activation: Effect of visual feedback and stimulus delivery. Journal of Electromyography and Kinesiology 26, 73-81. Lynn S.K., Costigan P.A. (2009) Changes in the medial-lateral hamstring activation ratio with foot rotation during lower limb exercise. Journal of Electromyography and Kinesiology 19, e197-205. Lynn S.K., Noffal G.J. (2012) Lower extremity biomechanics during a regular and counterbalanced squat. Journal of Strength and Conditioning Research 26, 2417-2425. Matheve T., Brumagne S., Timmermans A.A.A. (2017) The effectiveness of technology-supported exercise therapy for low back pain: A systematic review. American Journal of Physical Medicine & Rehabilitation 96, 347-356. Merletti, R., Wallinga, W., Hermens, H.J. and Freriks, B. (1997) Guidelines for reporting SEMG data. In: The state of the art on sensors and sensor placement procedures for sur- face electromyography: a proposal for sensor placement procedures, deliverable of the SENIAM project. Published by Roessingh Research and Development bv. ISBN 90-75452-15-2. Michener L.A., Sharma S., Cools A.M., Timmons M.K. (2016) Relative scapular muscle activity ratios are altered in subacromial pain syndrome. Journal of Shoulder and Elbow Surgery 25, 1861-1867. Milner-Brown H.S., Stein R.B. (1975) The relation between the surface electromyogram and muscular force. Journal of Physiology 246, 549-569. Park Y.K., Kim J.H. (2017) Effects of kinetic chain exercise using EMG-biofeedback on balance and lower extremity muscle activation in stroke patients. The Journal of Physical Therapy Science 29, 1390-1393. Pascoe M.A., Holmes M.R., Stuart D.G., Enoka R.M. (2014) Discharge characteristics of motor units during long-duration contractions. Experimental Physiology 99, 1387-1398. Roberts D., Kuenze C., Saliba S., Hart J.M. (2012) Accessory muscle activation during the superimposed burst technique. Journal of Electromyography and Kinesiology 22, 540-245. Snarr R.L., Hallmark A.V., Casey J.C., Esco M.R. (2017) Electromyographical comparison of a traditional, suspension device, and towel pull-up. Journal of Human Kinetics 58, 5-13. Staudenmann D., Roeleveld K., Stegeman D.F., van Dieen J.H. (2010) Methodological aspects of SEMG recordings for force estimation – A tutorial and review. Journal of Electromyography and Kinesiology 20, 375-387. Walker S., Davis L., Avela J., Hakkinen K. (2012) Neuromuscular fatigue during dynamic maximal strength and hypertrophic resistance loadings. Journal of Electromyography and Kinesiology 22, 356-362. Yang J.F., Winter D.A. (1983) Electromyography reliability in maximal and submaximal isometric contractions. Archives of Physical Medicine and Rehabilitation 64, 417-420. Zeller B.L., McCrory J.L., Kibler W.B., Uhl T.L. (2003) Differences in kinematics and electromyographic activity between men and women during the single-legged squat. American Journal of Sports Medicine 31, 449-456.

Athos Science: Summary of Validity and Reliability

Dr. Daniel Feeney

By Dr. Daniel Feeney <h2 id="validity-and-reliability-of-semg">Validity and Reliability of sEMG</h2> Surface electromyography (EMG) measures the electrical activity produced by the muscles when they are activated by the nervous system. Until recently, there was no way to easily and reliably record EMG in a field setting or even just away from the laboratory or training room. Athos has changed this. Athos wearable technology for sports performance has embedded sEMG sensors that record sEMG signals that are as accurate and reliable as a research-grade system. <h2 id="experiments">Experiments</h2> We asked participants to visit the laboratory on seven separate occasions and perform isokinetic knee flexion and extension (kicking in and out) at 50, 75, and 100% MVC. We assessed reliability (how consistent the EMG signals were across days) and validity (how Athos’s signal compared with Biopac and how both systems were associated with torque). We found no significant difference between systems. <img src="https://athos-website.s3.amazonaws.com/v2/content/V%26RBlog.png" alt="alt alt alt"> Figure 1. Comparison of RMS EMG amplitude over time during a representative set at 60 deg/s. From top to bottom, vastus medialis (RVM), vastus lateralis (RVL), bicep femoris (RBF) and torque output. <h2 id="what-this-means-for-coaches">What This Means for Coaches</h2> The information in the EMG signal can provide pivotal insights regarding muscle activity and recruitment patterns. Athos proved to be valid and as reliable as a research-grade system, while being portable and easy to use. Coaches and athletes can use Athos to record EMG signals to understand muscle coordination patterns and the internal load required to perform at practice, in the weight room, and during games. Coaches can use Athos EMG as a measure of internal load to periodize their team’s training schedule. By monitoring key muscle ratios such as gluteus maximus:hamstring and quadricep:hamstring ratios, Athos EMG garments can be used to be proactive about injury risk. <h2 id="if-you-d-like-to-learn-more-about-athos-please-click-here-"><a href="http://resource.liveathos.com/monitor-glute-to-hamstring-ratio-to-support-healthy-athletes">IF YOU'D LIKE TO LEARN MORE ABOUT ATHOS PLEASE CLICK HERE </a></h2>

Athos Science: sEMG Overview by Coach Garrett

Coach Garrett Nelson

<h2 id="athos-science-semg-overview-by-coach-garrett">Athos Science: sEMG Overview by Coach Garrett</h2> Trainers and sport scientists that we partner with have asked us about the science behind Athos. We decided to leverage the knowledge of our research team and coaches to breakdown what Athos measures and how. In this first of many microblogs covering Athos Science, we’ll break down what we mean by muscular stress and how we use sEMG to measure it. In this overview, we will learn from Coach Garrett Nelson, founder and Strength and Conditioning Coach at Victory Athletics. Garrett was an early adopter of the Athos Training System and has a wealth of knowledge on what it’s measuring and how to apply Athos sEMG to his coaching decisions. <h2 id="using-athos">Using Athos</h2> As a Strength and Conditioning Coach, I’ve spent a lot of time researching and experimenting with what the best tools are for sports performance. I’ve also spent a lot of time trying to understand important insights and training variables and how I can leverage them to keep my athletes healthy and performing and practicing at their best. When I was introduced to Athos, I was really excited to be able to get my athletes in the gear and run quick experiments as well as increase my understanding of how each of their bodies worked to produce movement. This ability combined with metrics such as measuring internal muscular stress, muscular ratios, sequencing patterns, and acute to chronic ratios allows me to measure, adjust, and re-evaluate training programs constantly. On top of that, having real time biofeedback has proven extremely helpful and, honestly, fun. Everyone wants to see their biceps turn red when they start flexing! To provide all of this data, Athos leverages (sEMG) technology. sEMG is a tool typically used by researchers and clinicians to better understand how a given task or skill is accomplished by the body through the measurement of electrical activity. This electrical activity is needed to create any kind of movement, which in turn we use to help us better understand how the body is “stressed.” <h2 id="semg-measures-muscular-stress">sEMG Measures Muscular Stress</h2> We use sEMG like Athos to help us understand and estimate overall stress because of the proven relationships that sEMG has with other variables. With Athos, when we use the term “stress” we refer to a general definition meaning pressure or tension exerted on the physical and physiological systems of the body. During training and sport, the neuromuscular system causes the production of force across muscle groups to support and overcome the load and demands on the athlete. <h2 id="breakdown-of-stress">Breakdown of Stress</h2> We’ve come up with three areas to help us better understand how to break down the relationships and measurements of stress with Athos. These areas are: <ol> <li>Physical </li> <li>Metabolic </li> <li>Neurological </li> </ol> <h2 id="physical-stress">Physical Stress</h2> Physical Stress refers to the mechanical tension and the structural breakdown of muscle, typically from force production. This can be estimated by understanding the correlation between sEMG and force production (positive correlation - more sEMG, more force). By wearing the gear during a weight room session, I can better understand: relative muscular contributions, training balance and asymmetries (left to right, anterior to posterior, etc), and an estimate of how much each muscle group was used during the session. By analyzing these variables, Athos can be used to plan volume loads, track imbalances, intent, and help us understand if and when an athlete is doing too much or too little. This has implications throughout the competitive season, from preparing during the off season, to tapering pre-season, to maintaining ability in season, to recovering in the off season. The more data we accumulate, the more effective and individualized each program can become. <h2 id="metabolic-stress">Metabolic Stress</h2> Metabolic stress refers to the use of the energy available to the body, both locally (in the muscle) and systemically (stored and released into the bloodstream). This can be estimated by understanding the relationship between sEMG, fatigue, lactate buildup (fuel consumption and repletion), heart rate, and an understanding of both local muscular level and general systemic use of energy. For example, the longer my sessions are (on field or in the weight room), the more my muscles are used, the higher chance of using up more fuel and having some level of metabolic distress. The body’s energy systems take time to restore and Athos can help us understand not only how much rest time we should take, but also if we need to increase our post training nutrition dependent on the upcoming schedule. If I could know early in the game that my star player’s energy levels would not sustain him to take a game winning shot, you had better believe I’m finding a way to help him replete and recover enough to have him on court when we need him the most. <h2 id="neurological-stress">Neurological Stress</h2> The neurological system, in this sense, refers to the ability to produce skill and muscular function properly. Neurological stress is produced by all activities, with high velocity, high force, and high skill activities taxing the system more than general movements (imagine a squat vs. a pitcher throwing a baseball). This can be estimated by seeing greater sEMG activity to produce the same or a lesser outcome for a given task, and can be measured both acutely - within a day or a session - and chronically over time. Both cases require rest to help reduce fatigue and restore or increase performance, and the time needed is dependent on how deep the fatigue hole has been dug. As an acute example, if a starting pitcher’s motor pattern and workload is too high in the 6th inning, now I have the opportunity to do something about it and make a more informed managerial decision. As a chronic example, if my star reliever has thrown several days in a row and isn’t showing the same physiological patterning, now I can think logically about whether it is a good idea to put him in or let him rest. Example figures demonstrating how these components fit together to cause a muscle to produce force. Could combine this into a body infographic view. <h2 id="take-away">Take Away</h2> Athos helps us to better understand what is happening to our athletes in training. It takes the guesswork out of potential causes of decreased performance and can even be used to help predict injuries (relating to pattern changes due to fatigue, ACR, muscle breakdown, ect.) before the injuries can take athletes off the field. Athos helps us prepare, by knowing if and when to push, and when to back down. As well as what demands our athletes need to be ready for day in and day out during the season. Athos helps us analyze our data and programs at a group and individual level, keying in on the metrics that matter most to us. It also creates a history that shows changes over time and helps create better plans moving forward with an athlete. Gone are the days of amassing questionnaires, RPE, and tracked volume loads. Now we have a real-time resource in Athos that helps us to make better decisions, faster. <h2 id="if-you-d-like-to-learn-more-about-athos-please-click-here-"><a href="http://resource.liveathos.com/monitor-glute-to-hamstring-ratio-to-support-healthy-athletes">IF YOU'D LIKE TO LEARN MORE ABOUT ATHOS PLEASE CLICK HERE </a></h2> References Kent J. No Muscle Is an Island: Integrative Perspectives on Muscle Fatigue. Med Sci Sports Exerc. 2016 Carroll T. J. Recovery of central and peripheral neuromuscular fatigue after exercise. J Appl Physiol 2016.

Athos Science: Top Hamstring Recovery Tips

By Garrett Nelson, Strength & Conditioning Coach and Owner at Victory Athletics

By Garrett Nelson, Strength & Conditioning Coach and Owner at Victory Athletics Hamstring injuries are always lurking. On top of that, once you’ve injured your hamstring, there is a substantially higher chance of doing it again, especially if not strengthened properly. Unfortunately, there is a massive range of severity involved, issues can range from a few days of limping to a few months of full rehabilitation and recovery. Hamstrings never discriminate who they affect, and the best defense is a good offense! To help speed up the recovery and decision making process, we’ve created a priority list based on our experience working with athletes and teams: <img src="https://athos-website.s3.amazonaws.com/v2/content/GarrettBlogHam2.jpg" alt="alt alt alt"> <h2 id="3-priorities-when-coming-back-from-a-hamstring-injury-">3 Priorities when coming back from a hamstring injury:</h2> #1 Restoring hamstring length and function (progressing to eccentric resistance) Range of motion is the first priority, as it allows full function of the muscle, and re-strengthening can begin. Each muscle action of the hamstring needs to be reintroduced based on pain and recovery timelines, and progressed to build strength as quickly as possible. # 2 Glute strength and sequencing Lack of glute strength and improper sequencing can easily contribute to hamstring issues, so proper strengthening and restoration of function can help mitigate undue stress upon the hamstring. #3 Reactivity of the hamstring in ballistic movements After strength in a controlled environment is produced, the athlete has to begin dynamic strength and stability exercises to impose the specific demand on the muscles seen in their respective activity. This is where sport and sport-like activities will start to be incorporated and progressed based on each individual situation. Whether it’s deciding what to program, adjusting the cueing during movements, tracking training load and progress, or understanding imbalances and muscle contribution, Athos has helped coaches and athletes achieve their goals. In short, those with stronger, properly trained hamstrings reduce their risk of injury, and can recover faster if they were to hurt it. <img src="https://athos-website.s3.amazonaws.com/v2/content/HamHacks.png" alt="alt alt alt"> <h2 id="3-exercises-to-help-strengthen-and-prevent-hamstring-injuries">3 exercises to help strengthen and prevent hamstring injuries</h2> #1 Nordic hamstring curls/glute ham raises These can easily be called the greatest hamstring exercises in the history of ever. The biggest bang for your buck here is that these are great eccentric loading exercises for the hamstrings, which is the function of 99% of athletes during athletic movements. The stronger they can resist eccentric tension, the stronger they can brake or produce force. Have a beginner athlete? Good news - there are tons of ways to regress the movement to make them accessible to every level. If that isn’t enough reason to find a way to incorporate them into your training, there is also a myriad of scholarly studies on programs involving these exercises and how and why some of the largest professional athletic organizations use them. #2 Stability ball/TRX/slider leg curls This is an exercise variation involving knee flexion combined with hip extension. The hamstring usually assists with hip extension but another function is the ability flex/resist flexion of the knee while the hip is already extended. This is also one of the few closed chain knee flexion exercises, so force is generated involving/through the ground instead of typical leg curls which are open chain (not connected to the ground). As they become easier, they can become more explosive, single leg, faster, resisted, you name it. #3 Single Leg RDLs Hamstrings also need to function independently. This is a closed chain balance/force production exercise that demands massive levels of coordination and stability at the hip. It is very easy to progress and regress, and highlights a lot of issues with whole body coordination. Once skill is decent, it can quickly be used as a multi-faceted warm up exercise to help prepare the hamstrings/posterior chain and hip stabilizers for greater demands of movement. <h2 id="want-athos-for-your-team-try-our-14-day-free-trial-"><a href="http://resource.liveathos.com/semg-for-sports-performance-and-player-readiness-14-day-trial">Want Athos for your team? Try our 14 Day Free Trial </a></h2>

Athos Q&A: Kieron Achara, Glasgow Rocks

Athos Contributor

<h2 id="kieron-achara-captain-of-glasgow-rocks">Kieron Achara, Captain of Glasgow Rocks</h2> The Glasgow Rocks are a professional basketball team based in Glasgow, Scotland. Every athlete on the Rocks roster has been wearing Athos gear in training, practices and games to monitor internal load to better understand individual performance. By looking at the entire team’s training load, their Sport Scientist, Kurtis Finlay, has been able to identify when there’s an athlete that needs special attention. He then analyzes the why to inform programming and load management decisions. Kieron, the oldest on the roster and captain of the team, has benefited from Kurtis’s weekly reports. Athos revealed a massive imbalance Kieron was battling. Since this surfaced, they’ve been able to make adjustments to prehab this issue before it became an injury keeping Kieron the healthiest he’s been during a season throughout his career. <img src="https://athos-website.global.ssl.fastly.net/v2/content/Screen%20Shot%202018-02-06%20at%2011.34.49%20AM.png" alt="alt alt alt"> Learn more about how Kieron has benefited from these muscular stress insights as well as why he feels that having his entire team in Athos gear is a competitive advantage. <h2 id="qa-with-kieron">Q&A with Kieron</h2> 1. Since using Athos in training and performance, what issues have you been able to identify? To start, something I love about Athos is that powerful visual of real-time biofeedback. Seeing the way my muscles are firing is amazing. I immediately saw that there was some imbalance between my left and my right glute. Historically, I always seemed to get issues in my right abductor so the Athos data helped me to analyze what was going on. Now with Athos, I’ve actually gotten to see why that problem was arising and it was because my left side wasn’t firing the same way when I got fatigued. So for me, it was just getting to visually see the way my body was moving and then trying to fix things so I can correct movements and ultimately prevent injuries. <img src="https://athos-website.global.ssl.fastly.net/v2/content/Screen%20Shot%202018-02-06%20at%2011.34.29%20AM.png" alt="alt alt alt"> 2. What decisions did your trainers make after identifying that issue? It’s funny because we always kind of kept a scorecard kind of thing before. For example, we said how hard we were practicing and so forth and that wasn’t that successful. So when I said, “I’m tired, I feel this or that,” it was not until I saw absolutely everything in front of me on Athos live-view that I was able to see this. Kurtis, our Athletic Trainer who’s working with us, was essentially saying like “Look, at Kieron here and do you see what’s happening? You’re getting tired here, you need to rest.” He also put together a workout plan to try and help me eradicate those imbalances in my body. <img src="https://athos-website.global.ssl.fastly.net/v2/content/Screen%20Shot%202018-02-06%20at%203.00.00%20PM.png" alt="alt alt alt"> <img src="https://athos-website.global.ssl.fastly.net/v2/content/Screen%20Shot%202018-02-06%20at%202.56.58%20PM.png" alt="alt alt alt"> 3. What went into that workout plan to eradicate those imbalances? It was mainly to do with my posterior chain. It was more work on what we call preventative rehab. It was basically a lot of deadlifts and really anything to do with posterior chain. Specific arabesque stretches and what not. It was a lot of things that I’ve known about but I never really took seriously. I’ve got a 15 minute workout before every practice that I do to make sure my muscles are firing the right way, which has really helped me because I can feel it. You know people tell me do this and that, but until you actually see it, it really doesn’t click. I’ve tried a lot of different things in the past, in fact we had a GPS based system before, but this was the first visual technology that allowed me to see the way my muscles were actually working and in real-time. That was really an eye opener for myself. <h2 id="want-a-free-14-day-trial-with-athos-click-here"><a href="http://resource.liveathos.com/semg-for-sports-performance-and-player-readiness-14-day-trial">Want a free 14-day trial with Athos? Click here</a></h2> 4. How has your body felt since you’ve identified and addressed these issues? It’s been great. I’m one of the older guys on the team, in fact, I am the oldest guy on the team. I’ve been playing for a lot of years and there’s times that you feel your body is breaking down. But this year, because we’ve been able to use Athos to identify my weaknesses before they became injuries and then analyze the data to make decisions around adjustments I needed to make, I’ve been able to stay healthy. I’ve really worked hard on them and it’s actually been one of my busiest seasons yet. I also play for the Great Britain National team and I play for the Scottish national team, I’ve had tournaments and really no breaks at all. So to be fair, for not having any breaks and not having any injuries during this season, that goes to show you that Athos has been a great addition for me. 5. If you had to tell a new rookie on the team about Athos and the value it provides, what would you tell them? This is one of the things as a professional athlete that I know, which is that it’s very hard to get buy in. Players never believe they need anything until they’re hurt. Obviously, being one of the older ones on the team, for me it’s all about longevity and how long I can last and that’s kind of what I would preach to a rookie. Like ok, maybe you’re not hurt at this moment and time, but you can maybe start to see things that will help your game and that’s how you have to sell it. It’s not about just preventing injury, as a professional athlete you don’t really buy into that until you’ve really experienced it, but then it’s too late. But if you talk about how this can actually help improve your game, and how it can help improve your vertical jump, how it can help do these sort of things, that’s a different way of thinking and I think that’s the enhancer for a lot of the younger athletes. Looking at how much this can improve your game, if you can improve one or two percent, like with your speed, quickness and agility or whatever it may be, that’s the competitive advantage you need to make it to the next level. <img src="https://athos-website.s3.amazonaws.com/v2/content/IMG_6099.jpg" alt="alt alt alt"> 6. Do you trust that by using Athos you’re going to have a competitive advantage? I wholeheartedly agree. We are very fortunate in our situation to have complete access to a Physical Therapist and we’re very fortunate that Kurtis is here. But having Athos, it’s a real competitive advantage. Like I said, most people react to certain things, so this is actually being one step ahead and identifying things before they happen. You can see how your body is working, how it’s moving and not only that, you also get what you put into the game. I think the next thing for us as far as data analysis, will be actually looking at what’s going into wins and losses because I think once you start tapping into that you know how hard you can push people in practice in order to get that real competitive advantage. Like you know when you’re hitting this target, you’re going to win more games than you lose. That’s a really important data set. <img src="https://athos-website.global.ssl.fastly.net/v2/content/Screen%20Shot%202018-02-07%20at%203.48.45%20PM.png" alt="alt alt alt"> Figure: This graph illustrates Kieron Achara's workload across Dec & January games. The graph illustrates workload and intensity to be much greater during the Newcastle game on the 1/12/2017. Intensity appears to drop against teams when we are substantially ahead in points, which is expected (White et al., 2014). Overtime, and thus with more data, this shows promising results for comparison of intensity across games and outcome. 7. What’s your favorite thing about the data that Athos provides? I just love being able to look at my practice straight away. Sometimes I say, “Wow! I’ve done everything ok like my left glute and my right glute look very well balanced.” It’s seeing the transition between when I first started off seeing the imbalances in my body and now working on that, having it being corrected. I can see it visually while I’m actually reaching the targets I want to reach. 8. How often are you looking at your Athos data to help to inform your decisions? Kurtis works with me every week and provides feedback. He’s the first to tell me, “Hey, have you been doing your exercises?” He really stays on top of that for me which is so great to have. It’s a valuable addition to the team. 9. Does Athos interfere with your day to day at all? It’s simple enough to use. What I have seen, and this is the same when we did Catapult and other technologies, is that it’s one thing when you have Sport Scientist working for a team they only work when there’s buy in from the Coach. That’s the only way, so if the Coach believes in it, he’ll find the time to make sure everyone’s set up. But if they don’t believe in it, it gets very, very tricky because there’s constantly conflict. So I’m very fortunate that with our team at this moment, we’ve had a coaching change and our new Coach is 100% all in with Athos and just Sport Science in general. A lot of coaches are sort of dismissive with it which is very conflicting but I’ve been on a lot of teams who’ve used things in Sport Science and when you get the buy in from the main man, that makes a difference. 10. Are there any metrics or ratios that you like to track most? For me, essentially, I’m just looking at my body and the way it moves at this time. Kurtis has dove deeper into the games that we’re very successful in and looking at the work that’s been put into that. Subconsciously, that’s in the back of my mind even when I’m playing or in practice, I’m making sure I’m actually giving it my all because everything is getting monitored at all times. So it’s not like I can take plays off because the results will show that. I think subconsciously it actually helps you train a little bit harder because as an older player, a veteran player, it’s very easy to take some practices off and cut corners but when that’s actually getting watched and analyzed, it’s kind of a motivator to me. 11. If you played for another team, is Athos something you’d advocate for? To give you a perfect example, I’ve already done that with my national team. I’m on the Great Britain team and we used to have Catapult before and I have to say Catapult has nothing on Athos. Athos is a level above that. The Catapult stuff was good but this is a lot more specific. I would definitely recommend Athos. I already have with my Great Britain squad, and that’s something they’re looking to explore once they’re funding comes in as well. 12. Knowing your teammates are in Athos gear, does that give you confidence that they’re all working as hard and smart as they can be to put you guys in the best position to win? Yes, it’s a reassurance. I know that if someone’s at risk of injuring themselves, we’ll be able to identify it a lot quicker. The two points for me is I have the confidence that guys are getting challenged and there’s accountability because Kurtis can literally see if you haven’t been working hard or if you have been working hard. My thing is, if you’re working hard, you have to be working smart. So you need to know the body is working the way it should be working. <img src="https://athos-website.global.ssl.fastly.net/v2/content/Screen%20Shot%202018-02-07%20at%201.53.01%20PM.png" alt="alt alt alt"> A lot of people when they get really tired, it’s normal to say ok I’m going to ease off because your body is not working the way it should be. But if you have reassurance that your body is working the way it should be, you’re actually not as fatigued as you think you are, it gives you a lot more to strive for and I think that’s one of the strong points of Athos. We have that reassurance that we’re getting looked after. Big brother is watching you and they’re making sure you’re healthy. <h2 id="want-athos-for-your-team-try-our-14-day-free-trial-"><a href="http://resource.liveathos.com/semg-for-sports-performance-and-player-readiness-14-day-trial">Want Athos for your team? Try our 14 Day Free Trial </a></h2>

Athos Spotlight: Glasgow Rocks

Athos Contributor

<h2 id="athos-training-system-for-teams">Athos Training System for Teams</h2> For a team to win games, the best players have to play. For the best players to play their best, they need to be healthy and perform. Responsibility for keeping them here belongs to the performance staff. This task proves to be easier said than done, for one, because large roster sizes make it near impossible to manage the day to day requirements of your athletes. This is where the staff needs help wherever they can, and performance technology is stepping up to that role in all new ways. New technology is helping training staff members keep track of these large groups by prioritizing where time and attention needs to be spent. This allows greater control of the group and helps flag any potential issues or understand when to push them harder. The<a href="https://www.glasgowrocks.co.uk"> Glasgow Rocks</a>, a professional basketball team in Scotland, have been using Athos both in practice and in game to track how their athletes are responding to the various stressors of a professional season. <h2 id="identify">IDENTIFY</h2> The Glasgow Rocks’ Athletic Trainer, Kurtis Ashcroft, was thrilled to try out Athos on these athletes because he felt ultimately it would help him to do his job more efficiently. <img src="https://athos-website.global.ssl.fastly.net/v2/content/IMG_6098.jpg" alt="alt alt alt"> Kurtis has every player wearing Athos gear, in fact, the team made it a requirement. This was great because it allowed Kurtis to have a rich data set for each athlete so that he could spot trends and notice inefficiencies. This really paid off when Kurtis and the training staff noticed one of their star players, Alasdair Fraser (Ali), was experiencing repercussions from a lingering right ankle issue. The trainers flagged this because a massive imbalance continued to occur during weight room training, practices and games. Kurtis knew that Ali had some right ankle issues in the past that had bothered him from time to time, but they didn’t think it was worth taking him out of games and dedicating a rehab program to his ankle. But this imbalance they identified proved that it was worth rehabbing his ankle so he could play at his optimal level in game to help the team win. Once Kurtis and team identified this issue being worthy of fixing, they formed a hypothesis that Ali was more heavily relying on his left side due to pain and weakness in the right ankle. As the right ankle pain became more prominent, his left imbalances increased. To stop this pattern, Ali’s game and practice time was first reduced and then a rehabilitation program was put into place to address the issues underlying the imbalance. <img src="https://athos-website.s3.amazonaws.com/v2/content/GlasgowRocks_Balance.jpg" alt="alt alt alt"> <h2 id="analyze">ANALYZE</h2> Kurtis and the rest of the training staff spent a lot of time discussing the findings from Athos. Once they flagged the imbalances, they could easily identify what the potential issues were as the injury came to light. Kurtis used the data to confirm that he did indeed need to be shutdown and that they needed an individualized rehab program set up for Ali to start his road to recovery. Ali was given a physiotherapist developed rehabilitation program that Kurtis was able to watch and modify as needed. Here's an example of a typical Monday's programming for Ali: Week Beginning Monday 1/15/2018 Gastroc Raise Double leg 3 x 10 Soleus Raise Double leg 3 x 10 Resisted inversion 3 x 10-20 Resisted Eversion 3 x 10-20 Single leg balance <img src="https://athos-website.s3.amazonaws.com/v2/content/ASGlasgow.png" alt="alt alt alt"> Figure 1. The left and right training load for practices and games for Alasdair. The orange (left) training load is consistently higher than the blue (right) side. Rehab for this injury started near 1/03. <h2 id="optimal-performance">OPTIMAL PERFORMANCE</h2> Ali’s imbalances improved ahead of schedule, within about two weeks of starting his Return to Play protocol, and the performance staff was impressed with how quickly he progressed. He no longer has any pain in his right ankle. Ali progressed above and beyond what was expected and was back on the court much sooner than the physio anticipated. He’s officially back in the gym doing the usual team sessions. Before every game, he does extra band work to make sure he’s properly warmed up. During the training warm up, he does a few ankle dominant exercises like dot drills, pogos, hop-sick-hold, and inch worms. Kurtis also does pair drills for fun which help loosen the soleus, hips and knees. Then Kurtis and Ali also did back-to-back squats and balance drills. All these movements have helped to keep Ali game ready and confident in his abilities. <img src="https://athos-website.global.ssl.fastly.net/v2/content/Screen%20Shot%202018-02-06%20at%2011.34.29%20AM.png" alt="alt alt alt"> <h2 id="maintaining-readiness">MAINTAINING READINESS</h2> After the balance normalized, we could try and understand what may have caused the original issues Ali experienced. These outliers gave the performance staff a plan to move forward with some test and retest opportunities. Since this rehab, they’ve been looking into a different issue that was identified by using Athos. Ali’s quad contribution is overcompensating left. His hamstring and posterior chain contribution is greater on the left side. Kurtis had Ali do some tuck jumps as a part of his warm ups to help with this. He complained that this aggravated his lower back and has been aggravating ever since. He has a weakness in his posterior chain. There’s some hypothesising around whether this stemmed from his right ankle or is an underlying issue. Moving forward the performance staff plans to continue monitoring Ali during training to see how they can use the data to inform their decisions with his programming. Having Athos to help identify issues with players was key to maintaining health and keeping the best players in the game. If you’d like to learn more about how to use Athos to keep your players performing at their best you can click here to learn more. <h2 id="want-athos-for-your-team-try-our-14-day-free-trial-"><a href="http://resource.liveathos.com/semg-for-sports-performance-and-player-readiness-14-day-trial">Want Athos for your team? Try our 14 Day Free Trial </a></h2>

Athos Science: 5 Glute Tips