Monitoring Arm Movements Post-Stroke for Applications in Rehabilitation and Home Settings

Abstract

Optimal recovery of arm function following stroke requires patients to perform a large number of functional arm movements in clinical therapy sessions, as well as at home. Technology to monitor adherence to this activity would be helpful to patients and clinicians. Current approaches to monitoring arm movements are limited because of challenges in distinguishing between functional and non-functional movements. Here, we present an Arm Rehabilitation Monitor (ARM), a device intended to make such measurements in an unobtrusive manner. The ARM device is based on a single Inertial Measurement Unit (IMU) worn on the wrist and uses machine learning techniques to interpret the resulting signals. We characterized the ability of the ARM to detect reaching actions in a functional assessment dataset (functional assessment tasks) and an Activities-of-Daily-Living (ADL) dataset (pizza-making and walking task) from 12 participants with stroke. The Convolutional Neural Network (CNN) and Random Forests (RF) classifiers had a Matthews Correlation Coefficient score of 0.59 and 0.58 when trained and tested on the functional dataset, 0.50 and 0.49 when trained and tested on the ADL dataset, and 0.37 and 0.36 when trained on the functional dataset and tested on the ADL dataset, respectively. The latter is the most relevant scenario for the intended application of training during a clinical visit for monitoring movements in the in-home setting. The classifiers showed good performance in estimating the time spent reaching and number of reaching gestures and showed low sensitivity to irrelevant arm movements produced during walking. We conclude that the ARM has sufficient accuracy and robustness to merit being used in preliminary studies to monitor arm activity in rehabilitation or home applications.

Keywords

• Accelerometer
• home monitoring
• arm rehabilitation
• reach detection
• machine learning