How can AI reduce wrist injuries in the workplace?

TL;DR

This paper develops an EMG-based control strategy for a wearable wrist exoskeleton aimed at reducing workplace injuries by classifying wrist movements and predicting exerted forces using sensor data from industrial workers.

Contribution

It introduces a novel, simplified control and sensor strategy for wrist exoskeletons using EMG and force data, optimized for industrial application and minimal sensor use.

Findings

Wrist motion classification accuracy achieved with surface EMG data.

Force prediction model correlates exerted force with EMG signals.

Proposed control strategy supports effective wrist assistance in industrial settings.

Abstract

This paper explores the development of a control and sensor strategy for an industrial wearable wrist exoskeleton by classifying and predicting workers' actions. The study evaluates the correlation between exerted force and effort intensity, along with sensor strategy optimization, for designing purposes. Using data from six healthy subjects in a manufacturing plant, this paper presents EMG-based models for wrist motion classification and force prediction. Wrist motion recognition is achieved through a pattern recognition algorithm developed with surface EMG data from an 8-channel EMG sensor (Myo Armband); while a force regression model uses wrist and hand force measurements from a commercial handheld dynamometer (Vernier GoDirect Hand Dynamometer). This control strategy forms the foundation for a streamlined exoskeleton architecture designed for industrial applications, focusing on simplicity, reduced costs, and minimal sensor use while ensuring reliable and effective assistance.