For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called “synergy extrapolation”). This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called “included” muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called “excluded” muscle excitations). Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called “muscle excitations”), may reduce the redundancy of muscle excitation solutions predicted by optimization methods.
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