Abstract:Quantifying
post-stroke patient motor function is important for assessing
rehabilitation progress and optimizing the behavior of adaptive
rehabilitation robots. To this end, researchers have increasing turned
to the concept of muscle synergies, which encodes the simplified
neuromuscular control strategy employed by the central nervous system in
response to post-stroke impairment. In essence, the assessment metrics
should possess two key attributes: the ability to differentiate between
individuals in the pathological and healthy groups, and the capacity to
yield consistent measurements within the same individual, thereby
facilitating the refinement of adaptive control algorithms. Recent
findings have indicated that employing manifold similarity measurements
can enhance the class separability and intra-class compactness for the
classification/clustering algorithm. Consequently, we hypothesize that
evaluating synergy and synergy activation similarities, while
considering the underlying manifold structure, will render a more
sensitive and reliable approach for quantifying motor function in
post-stroke patients. To validate our hypothesis, we conducted a study
involving twenty healthy subjects and ten post-stroke patients. Our
results demonstrate that the utilization of manifold similarities leads
to superior outcomes compared to conventional metrics based on muscle
synergy. Specifically, we observed higher sensitivity (
gw v.s. Sw
,
0.0457 v.s. 0.0030
), greater intra-subject reliability (
gc v.s. Sc
,
0.6060 v.s. 0.1081
), and stronger correlations with clinical scores (
gw v.s. Sw
,
0.7588 v.s. 0.6249
) than conventional metrics. Therefore, the proposed similarity metrics
may be promising for transferring to adaptive control of rehabilitation
robots.
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