Model-based estimation of ankle joint stiffness during dynamic tasks
A validation-based approach
Christopher P. Cop (University of Twente)
Guillaume Durandau (University of Twente)
Alejandro Moya Esteban (University of Twente)
Ronald C. Van 't Veld (University of Twente)
Alfred Schouten (University of Twente, TU Delft - Biomechatronics & Human-Machine Control)
Massimo Sartori (University of Twente)
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Abstract
Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel 'perturbation-free' stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses.