Abstract
Determination of muscle forces during motion can help to understand motor control, assess pathological movement, diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in vivo measurement made computational analysis become a common alternative in which, as several muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle synergy structure within an SO framework improves prediction of muscle activations during walking. A motion/force/electromyography (EMG) gait analysis was performed on five healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, muscle–tendon kinematics, and moment arms. Muscle activations were then estimated using SynO with two to six synergies and traditional SO, and these estimates were compared with EMG measurements. Synergy optimization neither improved SO prediction of experimental activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was performed on SO estimated activations, being found that the reconstructed activations produced poor matching of experimental activations and joint moments. As conclusion, it can be said that, although SynO did not improve prediction of muscle activations during gait, its reduced dimensional control space could be beneficial for applications such as functional electrical stimulation or motion control and prediction.
Highlights
Knowledge of muscle forces during human movement could elucidate basic principles of human motor control (Pierrynowski and Morrison, 1985), facilitate assessment of pathological movement and diagnosis of neuromuscular disorders, and improve estimation of the loads experienced by diseased or injured joints (Hardt, 1978)
This study evaluated whether imposition of a synergy structure on muscle activations estimated via inverse-dynamics optimization (i.e., synergy optimization (SynO)) produces muscle activation estimates that are more consistent with EMG measurements than are those produced by traditional static optimization (SO)
The joint moments obtained from SynO using two through six synergies matched the inverse-dynamics joint moments well (Table 1, Figure 3)
Summary
Knowledge of muscle forces during human movement could elucidate basic principles of human motor control (Pierrynowski and Morrison, 1985), facilitate assessment of pathological movement and diagnosis of neuromuscular disorders, and improve estimation of the loads experienced by diseased or injured joints (Hardt, 1978). Because more muscles than degrees of freedom (DOFs) exist in the human musculoskeletal system, an infinite number of recruitment patterns are Muscle Synergies Optimization possible mathematically. This problem is often referred to as the muscle redundancy problem (Damsgaard et al, 2006) or force-sharing problem (Dul et al, 1984). Recent studies have demonstrated that the central nervous system (CNS) appears to use muscle synergies to simplify neural control of movement by coupling muscle activations together (Merkle et al, 1998; Shourijeh et al, 2016b; Barroso et al, 2017). The use of muscle synergy information for neurorehabilitation remains controversial, as the muscle synergy hypothesis is difficult to prove or falsify (Tresch and Jarc, 2009; Kutch and Valero-Cuevas, 2012)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
