EMG Pattern Classification to Control a Hand Orthosis for Functional Grasp Assistance after Stroke

TL;DR

This study demonstrates that EMG pattern classification can effectively control a wearable hand orthosis, enabling stroke survivors to perform grasping tasks and supporting functional rehabilitation.

Contribution

It introduces an EMG-based control system using a commodity forearm band for a wearable exotendon device, facilitating intuitive grasp assistance after stroke.

Findings

High accuracy in detecting user intent to open the hand

Successful execution of pick and place tasks

Feasibility of EMG control for functional rehabilitation

Abstract

Wearable orthoses can function both as assistive devices, which allow the user to live independently, and as rehabilitation devices, which allow the user to regain use of an impaired limb. To be fully wearable, such devices must have intuitive controls, and to improve quality of life, the device should enable the user to perform Activities of Daily Living. In this context, we explore the feasibility of using electromyography (EMG) signals to control a wearable exotendon device to enable pick and place tasks. We use an easy to don, commodity forearm EMG band with 8 sensors to create an EMG pattern classification control for an exotendon device. With this control, we are able to detect a user's intent to open, and can thus enable extension and pick and place tasks. In experiments with stroke survivors, we explore the accuracy of this control in both non-functional and functional tasks. Our results support the feasibility of developing wearable devices with intuitive controls which provide a functional context for rehabilitation.