Classifying and tracking rehabilitation interventions through machine-learning algorithms in individuals with stroke

Victor C Espinoza Bernal; Shivayogi V Hiremath; Bethany Wolf; Brooke Riley; Rochelle J Mendonca; Michelle J Johnson

doi:10.1177/20556683211044640

Classifying and tracking rehabilitation interventions through machine-learning algorithms in individuals with stroke

J Rehabil Assist Technol Eng. 2021 Oct 7:8:20556683211044640. doi: 10.1177/20556683211044640. eCollection 2021 Jan-Dec.

Authors

Victor C Espinoza Bernal¹, Shivayogi V Hiremath^{1

2}, Bethany Wolf³, Brooke Riley³, Rochelle J Mendonca⁴, Michelle J Johnson^{5

6}

Affiliations

¹ Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, USA.
² Director of Personal Health Informatics & Rehabilitation Engineering Laboratory, Temple University, Philadelphia, PA, USA.
³ Friends of the Redeemer United, St Elizabeth, Jamaica.
⁴ Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
⁵ Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA, USA.
⁶ Director of Rehabilitation Robotics Lab, University of Pennsylvania, Philadelphia, PA, USA.

Abstract

Introduction: Stroke is the leading cause of disability worldwide. It has been well-documented that rehabilitation (rehab) therapy can aid in regaining health and function for individuals with stroke. Yet, tracking in-home rehab continues to be a challenge because of a lack of resources and population-scale demands. In order to address this gap, we implemented a methodology to classify and track rehab interventions in individuals with stroke.

Methods: We developed personalized classification algorithms, including neural network-based algorithms, to classify four rehab exercises performed by two individuals with stroke who were part of a week-long therapy camp in Jamaica, a low- and middle-income country. Accelerometry-based wearable sensors were placed on each upper and lower limb to collect movement data during therapy.

Results: The classification accuracy for traditional and neural network-based algorithms utilizing feature data (e.g., number of peaks) from the sensors ranged from 64 to 94%, respectively. In addition, the study proposes a new method to assess change in bilateral mobility over the camp duration.

Conclusion: The results of this pilot study indicate that personalized supervised learning algorithms can be used to classify and track rehab activities and functional outcomes in resource limited settings such as LMICs.

Keywords: Artificial neural networks; classification; global health; low–middle-income country (LMIC); machine-learning; stroke.