首页|An Online Estimating Framework for Ankle Actively Exerted Torque Under Multi-DOF Coupled Dynamic Motions via sEMG
An Online Estimating Framework for Ankle Actively Exerted Torque Under Multi-DOF Coupled Dynamic Motions via sEMG
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NETL
NSTL
IEEE
Ankle rehabilitation robots can offer tailored rehabilitation training, and facilitate the functional recovery of patients. Accurate estimation of the actively exerted torque from the ankle joint complex (AJC) can increase the engagement of patients during rehabilitation training. Given the three degrees of freedom (DOFs) of AJC and its coupled motion, it becomes essential to accurately estimate the actively exerted torque under multi-DOF. This work introduces an estimation framework that includes the Hill-based sEMG-force model, the ankle musculoskeletal dynamic decoupling model, and the parameter identification-calibration strategy. The Hill-based sEMG-force model estimates the force generated by individual muscles involved in AJC; The parameter identification-calibration strategy combined with pre-experiment identifies unknown variables in the ankle musculoskeletal dynamic decoupling model; Finally, the musculoskeletal dynamic decoupling model relates the muscle forces to the AJC’s actively exerted torque. The musculoskeletal dynamic decoupling model combines anatomical and biomechanical features, enabling parameters derived from a single DOF pre-experiment through identification-calibration strategy to be applicable in multi-DOF dynamic motion. To evaluate the estimation performance of the framework, experiments were conducted in various directions involving both single and multiple DOFs. The results show that the proposed framework can estimate the actively exerted torque with a normalized root mean square error (NRMSE) of ${10}.{29}\% \pm {2}.{86}\%$ (mean ± SD) for torque estimation under a single DOF, and NRMSE of ${11}.{35}\% \pm {4}.{51}\%$ under multiple DOFs, compared to the actual measured values. This framework can improve human-robot interaction training and improve the effectiveness of robot-assisted ankle rehabilitation training. It can also provide accurate neuro-information and joint torque data for medical teams, which can lead to early diagnosis of diseases and patient-specific treatment protocols.
TorqueMusclesMusculoskeletal systemAnkleTrainingDynamicsForceEstimationBiological system modelingComputational modeling
Yu Zhou、Jianfeng Li、Shiping Zuo、Jie Zhang、Mingjie Dong、Zhongbo Sun
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Beijing Key Laboratory of Advanced Manufacturing Technology, College of Mechanical & Energy Engineering, Beijing University of Technology, Beijing, China
Centre for Wireless Innovation, Queen’s University Belfast, Belfast, U.K.
Department of Control Engineering, Changchun University of Technology, Changchun, China
NETL
NSTL
IEEE
