Monday, November 18, 2019 Does Everyone Have a Unique Muscle Activation 'Fingerprint?' Researchers Say Yes In this review: Individuals have unique muscle activation signatures as revealed during gait and pedaling (Journal of Applied Physiology, October 2019 ) The message It's no secret that people move differently, but researchers who carefully tracked muscle movements of study participants during exercise think the differences may go even deeper than variation in movement styles. Their conclusion: humans possess muscle activation "signatures" that are as unique to each individual as fingerprints or iris structure. Not only could these patterns be used to identify an individual, they write, but finding a person's activation strategies could help to identify the potential for future musculoskeletal problems, and better tailor treatments to individual patient needs. The study Researchers analyzed movement patterns of 53 individuals using surface electromyography (EMG) on their legs as they pedaled on a stationary bicycle and walked on a treadmill. Using a machine learning protocol, authors of the study tracked activation patterns from 8 muscles of the right leg: the vastus lateralis (VL), rectus femoris (RF), vastus medialis (VM), gastrocnemius lateralis (GL), gastrocnemius medialis (GM), soleus (SOL), tibialis anterior (TA), and biceps femoris-long head (BF). They used the data to establish unique muscle activation signatures recorded during an initial session. Participants then returned for a second round of the same activities between 1 and 41 days after the first (average, 13 days), allowing researchers to evaluate the similarities between activation patterns observed at each session. Participants were in good health. Most were male (77%), with an average age of 23.1 years and average BMI of 23.2 for males and 21 for females. Findings Researchers found "substantial" variability in activation patterns among individuals, especially in the RF, GL, BF, and SOL muscles, with the same types of variability recorded on both days of activity. The machine learning system was able to identify individual muscle activation patterns during the first session with a high degree of accuracy, particularly when more of the tracked muscles were factored into the mix. The classification rate was just over 99% for pedaling and 98.86% for treadmill gait. Recognition rates were nearly as accurate when focused on the second session, where accuracy was 89.80% for 7 muscles in pedaling, and 86.20% for 7 muscles during walking. Authors of the study think the differences between the first and second sessions are due to variations in placement of the EMG sensors, but they believe that given the highly similar results, the differences in placement only strengthen their conclusions. The RF, GM, GL and SOL muscles provided the best recognition data for pedaling, while the TA and BF muscles were tied strongly to better recognition data related to gait. Why it matters Earlier studies have established that movement patterns such as gait can be consistently linked with individuals—a kind of signature—but those studies stopped short of an examination of identifying the muscle activation strategies that may (or may not) influence the movement pattern. Authors of the EMG study believe theirs is the first to look into activation itself as a biomarker. Although they call for further study, authors believe that individual muscle activation signatures may have "specific mechanical effects on the musculoskeletal system" and could help identify individuals who are at greater risk of musculoskeletal disorders. For example, they write, the activation patterns of the GM, GL, and SOL muscles tended to vary significantly between individuals; because these muscles are attached to the Achilles tendon in different fascicle bundles, "different activation strategies might induce unique load patterns of load distribution within the Achilles tendon, with some strategies being more likely to lead to tendon problems." More from the study Authors didn't land on a single explanation for why muscle activation patterns might be individualized, but they write that both "optimal feedback control" and "good enough" theories of motor control could be at play in activation signatures. Activation patterns may be consistent with the optimal feedback control theory in that "it is possible that each individual optimizes their movement with the muscle activation strategies that are best, given that individual's mechanical and/or neural restraints," they write. On the other hand, they add, it's also possible that the signatures develop according to the "good-enough" concept, "through motor exploration, experience, and training, leading to habitual rather than optimal strategies." It's a debate that likely won't be settled without "retrospective studies on large cohorts or longitudinal studies performed at different lifespans," authors note. Keep in mind… The study population was small, and homogenous. While the homogeneity was intentional to tease out the accuracy of the machine learning process, the approach limited researchers' ability to identify potential motor control theories at play, and whether at least some of the activation strategies are innate. Research-related stories featured in PT in Motion News are intended to highlight a topic of interest only and do not constitute an endorsement by APTA. For synthesized research and evidence-based practice information, visit the association's PTNow website.