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Principal postural acceleration and myoelectric activity: Interrelationship and relevance for characterizing neuromuscular function in postural control
Affiliation:3. Unit of Excellence in Well-Being and Health Innovation, School of Allied Health Sciences, University of Phayao, 19 Moo2 Maeka, Muang, Phayao 56000, Thailand
Abstract:One approach to investigating sensorimotor control is to assess the accelerations that produce changes in the kinematic state of the system. When assessing complex whole-body movements, structuring the multi-segmental accelerations is important. A useful structuring can be achieved through a principal component analysis (PCA) performed on segment positions followed by double-differentiation to obtain “principal accelerations” (PAs). In past research PAs have proven sensitive to altered motor control strategies, however, the interrelationship between PAs and muscle activation (surface electromyography, sEMG) have never been determined. The purpose of the current study was therefore to assess the relationship between PAs and sEMG signals recorded from muscles controlling the ankle joint during one-leg standing trials. It was hypothesized that medium correlation should be observed when accounting for neurophysiologic latencies (electro-mechanical delay). Unipedal balancing on a level-rigid ground was performed by 25 volunteers. sEMG activities were recorded from the tibialis anterior, peroneus longus, gastrocnemius medialis, and soleus muscles of the stance leg. The first eight PA-time series were determined from kinematic marker data. Then, a cross-correlation analysis was performed between sEMG and PA time series. We found that peak correlation coefficients for many participants aligned at time delays between 0.116 and 0.362 s and were typically in the range small to medium (|r| = 0.1 to 0.6). Thus, the current study confirmed a direct association between many principal accelerations PA(t) and muscle activation signals recorded from four muscles crossing the ankle joint complex. The combined analysis of PA and sEMG signals allowed exploring the neuromuscular function of each muscle in different postural movement components.
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