IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025
Analysis of Disease-Induced Changes in Human Locomotor Patterns Through the Co-Joint Synergistic Attention Algorithm.
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Chen Jingyao, Wang Chen, Hou Zeng-Guang, Deng Pingye, Peng Liang, Zhang Pu, Xu Ningcun
Аннотация
OBJECTIVE: Aiming to quantify and analyze disease-induced alterations in human movement, we explored the co-joint synergy patterns in locomotion through a vision-based co-joint synergistic attention algorithm.
METHODS: We recruited 30 participants (including 15 post-stroke patients and 15 healthy individuals) and extracted their 3D visual motor data for the joint feature coupling by a serial attention module. And we designed a dual-stream classification module for preclassification based on the spatio-temporal characteristics of the data. Then we extracted the important co-joint synergy patterns by a looping mask module and the co-joint synergy variability score.
RESULTS: Through the co-joint synergistic attention algorithm, we found significant differences in joint synergy patterns between post-stroke patients and healthy individuals during upper and lower limb tasks. Furthermore, we obtained quantitative results on the effect of specific diseases on co-joint synergy patterns among healthy individuals and patients. The validity of the result was verified by comparing with the commonly used Non-negative Matrix Factorization (NMF) and the Muscle Synergy Fractionation (MSF) methods.
CONCLUSION: Specific diseases can cause changes in human movement patterns, and by the co-joint synergistic attention algorithm we can analyze the alterations in joint synergies and also quantify the importance of different synergy groups.
SIGNIFICANCE: This research proposes a new approach for identifying specific co-joint synergy patterns arising from disease-altered biomechanics, which provides a new targeted protocol for the rehabilitation process.
METHODS: We recruited 30 participants (including 15 post-stroke patients and 15 healthy individuals) and extracted their 3D visual motor data for the joint feature coupling by a serial attention module. And we designed a dual-stream classification module for preclassification based on the spatio-temporal characteristics of the data. Then we extracted the important co-joint synergy patterns by a looping mask module and the co-joint synergy variability score.
RESULTS: Through the co-joint synergistic attention algorithm, we found significant differences in joint synergy patterns between post-stroke patients and healthy individuals during upper and lower limb tasks. Furthermore, we obtained quantitative results on the effect of specific diseases on co-joint synergy patterns among healthy individuals and patients. The validity of the result was verified by comparing with the commonly used Non-negative Matrix Factorization (NMF) and the Muscle Synergy Fractionation (MSF) methods.
CONCLUSION: Specific diseases can cause changes in human movement patterns, and by the co-joint synergistic attention algorithm we can analyze the alterations in joint synergies and also quantify the importance of different synergy groups.
SIGNIFICANCE: This research proposes a new approach for identifying specific co-joint synergy patterns arising from disease-altered biomechanics, which provides a new targeted protocol for the rehabilitation process.