Regressing force-myographic signals collected by an armband to estimate torque exerted by the wrist: A preliminary investigation

Human-machine-interfaces (HMI) have a key role for translating human intention into control commands to external devices. Different wearable techniques, including surface electromyography (sEMG), have been proposed for acquiring bio-signals that reveal the human intention. In this paper, we explore an easy-to-use wearable sensor device that can be used to measure force-myography (FMG) signals. We assess if FMG signals can be used to estimate isometric torque of hand pronation-supination, wrist flexion-extension or wrist radial-ulnar using a regression model. Results of our investigation report an average accuracy over 90%. The related standard deviation of 0.02 is showing consistency of data among different data collecting sessions. The proposed FMG-based device shows therefore promising performance for different future applications, which may include: monitoring the progress of patients during exercising in arm rehabilitation programs; proportional control of robotic hand prosthesis; and control of robot movements.

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