Joint torques estimation in human gait based on Gaussian process

• Published in: Technology and health care Vol. 31; no. 1; pp. 197 - 204
• Main Authors: Yang, Jiantao, Wang, Zekai, Sun, Tairen
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2023; Sage Publications Ltd
• Subjects: Active control; Algorithms; Biomechanical Phenomena - physiology; Data integration; Exoskeleton; Exoskeletons; Gait; Gait - physiology; Gaussian process; Humans; Joints (anatomy); Lower Extremity - physiology; Optimization; Prostheses; Prosthetics; Statistical analysis; Torque; Walking; Walking - physiology; Walking Speed - physiology; GP; human gait; joint torque; mechanics; prostheses; robots
• ISSN: 0928-7329; 1878-7401; 1878-7401
• DOI: 10.3233/THC-220190

BACKGROUND: Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions. OBJECTIVE: The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process. METHOD: The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs. RESULTS: The statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6 m/s). CONCLUSION: The results show that it is possible to estimate the joint torques at different scenarios.