EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses

Abstract

Objective. The limited functionality of hand prostheses remains one of the main reasons behind the lack of its wide adoption by amputees. Indeed, while commercial prostheses can perform a reasonable number of grasps, they are often inadequate for manipulating the object once in hand. This lack of dexterity drastically restricts the utility of prosthetic hands. We aim at investigating a novel shared control strategy that combines autonomous control of forces exerted by a robotic hand with electromyographic (EMG) decoding to perform robust in-hand object manipulation. Approach. We conduct a 3-day long longitudinal study with 8 healthy subjects controlling a 16-degrees-of-freedom robotic hand to insert objects in boxes of various orientations. EMG decoding from forearm muscles enables subjects to move, proportionally and simultaneously, the fingers of the robotic hand. The desired object rotation is inferred using two EMG electrodes placed on the shoulder that record the activity of muscles responsible for elevation and depression. During the object interaction phase, the autonomous controller stabilizes and rotates the object to achieve the desired pose. In this study, we compare an incremental and a proportional shoulder-decoding method in combination with two state machine interfaces offering different levels of assistance. Main results. Results indicate that robotic assistance reduces the number of failures by $41\%$ and, when combined with an incremental shoulder EMG decoding, leads to faster task completion time (median=16.9s), compared to other control conditions. Training to use the assistive device is fast. After one session of practice, all subjects managed to achieve tasks with $50\%$ less failures. Significance. Shared control approaches that give some authority to an autonomous controller on-board the prosthesis are an alternative to control schemes relying on EMG decoding alone. This may improve the dexterity and versatility of robotic prosthetic hands (RPHs) for people with trans-radial amputation. By delegating control of forces to the prosthesis' on-board control, one speeds up reaction time and improves the precision of force control. Such a shared control mechanism may enable amputees to perform fine insertion tasks solely using their prosthetic hands. This may restore some of the functionality of the disabled arm.

One-shot random forest model calibration for hand gesture decoding

“…Many myoelectric control systems use machine learning to map the electromyographic (EMG) signals [1][2][3][4][5] to control commands for human-machine interfaces, e.g. prosthesis [6][7][8][9][10][11][12] and virtual keyboards [13,14]. Most modern myoelectric control machine learning models require a large amount of data from a user to learn a bespoke and user-specific map [7,15,16].…”

One-Shot Random Forest Model Calibration for Hand Gesture Decoding

“…Many myoelectric control systems use machine learning to map the electromyographic (EMG) signals [1, 2, 3, 4, 5] to control commands for human-machine interfaces, e.g. prosthesis [6, 7, 8, 9, 10, 11, 12] and virtual keyboards [13, 14]. Most modern myoelectric control machine learning models require a large amount of data from a user to learn a bespoke and user-specific map [7, 15, 16].…”