A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation

Abstract

Stroke is one of the leading causes of disability among the elderly population and is a significant public health problem worldwide. The main impact of stroke is functional disabilities due to motor impairment after stroke. Advances in modern medicine and technology have significantly improved diagnosis and treatment; however, most post-stroke care is based on the effectiveness of rehabilitation. Stroke rehabilitation depends on two main components: (i) training (or therapy) to restore the patient to pre-stroke mobility and (ii) assessing motor functionality of affected patients performing activities to track motor recovery. This article highlights how combining wearable devices and machine learning (ML) produces new pathways for effective stroke rehabilitation. While wearable devices help capture patient movements at much finer time resolutions, ML allows us to build predictive models from wearable data to assist clinicians in diagnosis and treatments. Specifically, we expand on how wearable devices and ML can improve monitoring quality in training intervention, assessment, and remote monitoring. In addition, we provide our main findings from the literature, research challenges, and future directions in post-stroke therapies using wearable devices and ML.

Keywords

• Stroke rehabilitation
• wearable devices
• machine learning
• interventions
• remote monitoring