Human-Exoskeleton Coupling Dynamics of a Multi-Mode Therapeutic Exoskeleton for Upper Limb Rehabilitation Training

被引:12
作者
Xie, Qiaolian [1 ,2 ,3 ]
Meng, Qiaoling [1 ,2 ,3 ]
Zeng, Qingxin [1 ,2 ,3 ]
Fan, Yuanjie [4 ]
Dai, Yue [1 ,2 ,3 ]
Yu, Hongliu [1 ,2 ,3 ]
机构
[1] Univ Shanghai Sci & Technol, Rehabil Engn & Technol Inst, Sch Med Instrument & Food Engn, Shanghai 200093, Peoples R China
[2] Shanghai Engn Res Ctr Assist Devices, Shanghai 200093, Peoples R China
[3] Minist Civil Affairs, Key Lab Neural Funct Informat & Rehabil Engn, Shanghai 200093, Peoples R China
[4] Shanghai Elect Grp Cent Acad, Dept Rehabil Robot Prod, Shanghai 200070, Peoples R China
基金
中国国家自然科学基金;
关键词
Exoskeletons; Training; Robots; Mathematical model; Torque; Dynamics; Stroke (medical condition); Human-exoskeleton coupling dynamics; parameter identification; rehabilitation training; MODEL;
D O I
10.1109/ACCESS.2021.3072781
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The purpose of this study is to establish the human-exoskeleton coupling (HEC) dynamic model of the upper limb exoskeleton, overcome the difficulties of dynamic modeling caused by the differences of individual and disease conditions and the complexity of musculoskeletal system, to achieve early intervention and optimal assistance for stroke patients. This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient's height and weight and HEC dynamics parameters in the PPT mode. The RMSE of the elbow and shoulder joints of the prediction model is 9.5% and 13.3%. The proposed HEC dynamic model is helpful to provide different training effects in the PAT and PPT mode and optimal training and assistance for stroke patients.
引用
收藏
页码:61998 / 62007
页数:10
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