共 35 条
Hierarchical Optimization for Personalized Hand and Wrist Musculoskeletal Modeling and Motion Estimation
被引:0
作者:
Han, Lijun
[1
,2
]
Cheng, Long
[3
,4
]
Li, Houcheng
[1
,2
]
Zou, Yongxiang
[1
,2
]
Qin, Shijie
[1
,2
]
Zhou, Ming
[5
,6
]
机构:
[1] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China
[3] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
[4] Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
[5] Med Sch Chinese PLA, Beijing, Peoples R China
[6] Chinese Peoples Liberat Army Gen Hosp, Med Ctr 1, Dept Rehabil Med, Beijing, Peoples R China
基金:
中国国家自然科学基金;
中国博士后科学基金;
关键词:
Wrist;
Musculoskeletal system;
Muscles;
Read only memory;
Optimization;
Motion estimation;
Biomedical engineering;
Hierarchical optimization;
motion estimation;
personalized musculoskeletal model;
rehabilitation;
KNEE-JOINT ANGLES;
SENSITIVITY;
PREDICTIONS;
SIMULATIONS;
SOFTWARE;
D O I:
10.1109/TBME.2024.3456235
中图分类号:
R318 [生物医学工程];
学科分类号:
0831 ;
摘要:
Objective: Surface electromyography (sEMG) driven musculoskeletal models are promising to be applied in the field of human-computer interaction. However, due to the individual-specific physiological characteristics, generic models often fail to provide accurate motion estimation. This study optimized the general model to build a personalized model and improve the accuracy of motion estimation. Methods: Inspired by the coupling effect of wrist/hand movement, a hierarchical optimization approach for personalizing musculoskeletal models (HOPE-MM) is proposed, which aligns with the physiological characteristics of the human wrist and hand. To verify the effectiveness of personalized musculoskeletal model, single joint motions and simultaneous joint motions are estimated. In addition, Sobol sensitivity analysis is conducted to identify the key parameters of musculoskeletal model, providing guidance for model simplification. Results: The mean pearson correlation coefficient between the predicted joint angles and the measured joint angles are 0.95 $\pm$ 0.03 and 0.93 $\pm$ 0.01 for simultaneous wrist and metacarpophalangeal (MCP) joint movements, respectively, which have a significant improvement compared with the state-of-the-art works. By optimizing only the key parameters including tendon slack length, maximal isometric force and optimal fiber length, the performances of simplified model are comparable to the full-parameter model. Conclusion: These results provide insights into the effects of muscle-tendon parameters on musculoskeletal model, and musculoskeletal models personalized using hierarchical optimization methods can improve the accuracy of motion estimates. Significance: These findings facilitate the clinical application of musculoskeletal models in rehabilitation and robotic control.
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页码:454 / 465
页数:12
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