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.
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