We expanded upon experiments that investigated motor modularity in upper limb movements in humans. The identification of modular features, including distinct functional muscle weightings, during highly variable manipulation tasks supports a hypothetical neural mechanism in which the CNS combines preexisting basic patterns of muscle synergies rather than framing new patterns to deal with behavioral diversity in the upper limb.

Objective: To identify suggestions for future research on spinal movement variability (SMV) in individuals with low back pain (LBP) by investigating (1) the methodologies and statistical tools used to assess SMV; (2) characteristics that influence the direction of change in SMV; (3) the methodological quality and potential biases in the published studies; and (4) strategies for optimizing SMV in LBP patients. Methods: We searched literature databases (CENTRAL, Medline, PubMed, Embase, and CINAHL) and comprehensively reviewed the relevant papers up to 5 May 2020. Eligibility criteria included studies investigating SMV in LBP subjects by measuring trunk angle using motion capture devices during voluntary repeated trunk movements in any plane. The Newcastle-Ottawa risk of bias tool was used for data quality assessment. Results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Results: Eighteen studies were included: 14 cross-sectional and 4 prospective studies. Seven linear and non-linear statistical tools were used. Common movement tasks included trunk forward bending and backward return, and object lifting. Study results on SMV changes associated with LBP were inconsistent. Two of the three interventional studies reported changes in SMV, one of which was a randomized controlled trial (RCT) involving neuromuscular exercise interventions. Many studies did not account for the potential risk of selection bias in the LBP population. Conclusion: Designers of future studies should recognize that each of the two types of statistical tools assesses functionally different aspects of SMV. Future studies should also consider dividing participants into subgroups according to LBP characteristics, as three potential subgroups with different SMV characteristics were proposed in our study. Different task demands also produced different effects. We found preliminary evidence in a RCT that neuromuscular exercises could modify SMV, suggesting a rationale for well-designed RCTs involving neuromuscular exercise interventions in future studies.

The extent to which muscle synergies represent the neural control of human behavior remains unknown. Here, we tested whether certain sets of muscle synergies that are fundamentally necessary across behaviors exist. We measured the electromyographic activities of 26 muscles, including bilateral trunk and lower limb muscles, during 24 locomotion, dynamic and static stability tasks, and we extracted the muscle synergies using non-negative matrix factorization. Our results show that 13 muscle synergies that may have unique functional roles accounted for almost all 24 tasks by combinations of single and/or merging of synergies. Therefore, our results may support the notion of the low dimensionality in motor outputs, in which the central nervous system flexibly recruits fundamental muscle synergies to execute diverse human behaviors. Further studies are required to validate the neural representation of the fundamental components of muscle synergies.

The purpose of this study was to compare the muscle synergies extracted from 14 unilateral lower‐limb and trunk muscles between the first and final parts of a 400‐m sprint in experienced sprinters to understand neuromuscular coordination of multiple muscles in the fatigued condition sprint. Nine male 400‐m sprinters (400‐m personal record: 48.11 ± 1.6 s) performed 400‐m sprints as with the real competition strategy. We defined the first part (100–150 m section) and the final part (350–400 m section), and obtained mean spatiotemporal variables (e.g., running speed, step frequency, and step length) for both parts. Electromyography (EMG) signals were obtained using wireless EMG sensors (2000 Hz) from 14 lower‐limb and trunk muscles. Non‐negative matrix factorization was performed to extract the muscle synergies for both parts. We observed significantly declined spatiotemporal variables in the final part induced by fatigue. The extracted number of synergies was 7.0 ± 0.7 (mean ± SD) for the first part and 7.2 ± 0.4 for the final part with no significant differences between parts. However, we identified specific muscle synergy, and alterations in the individual muscle weightings of several hip muscles (rectus femoris: RF, tensor fasciae latae: TFL, and glutes maximus: Gmax muscles) while there was no change in the muscle weighting of shank muscles and the temporal patterns of all muscles even following fatigue in the 400‐m sprint. Fatigue‐induced performance decline in a 400‐m sprint corresponds to alterations in muscle synergies, particularly in hip muscles, with notable shifts in RF, TFL, and Gmax activation.