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Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model
Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model
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Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model
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Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model
Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model

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Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model
Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model
Journal Article

Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model

2025
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Overview
Human–machine interface (HMI) has been extensively developed and applied in rehabilitation. However, the performance of amputees on continuous movement decoding was significantly decreased compared with that of able-bodied individuals. To explore the impact of the absence of joint movements on the performance of HMI in rehabilitation, a generic musculoskeletal model (MM) was employed in this study to evaluate and compare the performance of subjects completing a series of on-line tasks with the wrist and metacarpophalangeal (MCP) joints unconstrained and constrained. The performance of the generic MM has been demonstrated in previous studies. The electromyography (EMG) signals of four muscles were employed as inputs of the generic MM to realize the continuous movement decoding of wrist and MCP joints. Ten able-bodied subjects were recruited to perform the on-line tasks. The completion time, the number of overshoots, and the path efficiency of the tasks were taken as the indexes to quantify the subjects’ performance. The muscle activation associated with the movement was analyzed. Across all tasks and subjects, the average values of the three indexes with the joints unconstrained were 7.7 s, 0.59, and 0.38, respectively, while those with the joints constrained were 17.86 s, 1.47, and 0.22, respectively. The results demonstrated that the subjects performed better with the wrist and MCP joints unconstrained than with those joints constrained in the on-line tasks, suggesting that the absence of joint movements can be a reason of the decreased performance of continuous movement decoding with HMIs. Meanwhile, it is revealed that the different performance on motion behaviors is caused by the absence of joint movements. Graphical abstract