aBnormal motION capture In aCute Stroke (BIONICS): A Low-Cost Tele-Evaluation Tool for Automated Assessment of Upper Extremity Function in Stroke Patients.

Authors

Syed A Zamin
Kaichen Tang
Emily A Stevens
Melissa Howard
Dorothea M Parker
Allyson Seals
Xiaoqian Jiang
Sean Savitz
Shayan Shams

Publication Date

9-1-2023

Journal

Neurorehabilitation and Neural Repair

Abstract

BACKGROUND: The incidence of stroke and stroke-related hemiparesis has been steadily increasing and is projected to become a serious social, financial, and physical burden on the aging population. Limited access to outpatient rehabilitation for these stroke survivors further deepens the healthcare issue and estranges the stroke patient demographic in rural areas. However, new advances in motion detection deep learning enable the use of handheld smartphone cameras for body tracking, offering unparalleled levels of accessibility.

METHODS: In this study we want to develop an automated method for evaluation of a shortened variant of the Fugl-Meyer assessment, the standard stroke rehabilitation scale describing upper extremity motor function. We pair this technology with a series of machine learning models, including different neural network structures and an eXtreme Gradient Boosting model, to score 16 of 33 (49%) Fugl-Meyer item activities.

RESULTS: In this observational study, 45 acute stroke patients completed at least 1 recorded Fugl-Meyer assessment for the training of the auto-scorers, which yielded average accuracies ranging from 78.1% to 82.7% item-wise.

CONCLUSION: In this study, an automated method was developed for the evaluation of a shortened variant of the Fugl-Meyer assessment, the standard stroke rehabilitation scale describing upper extremity motor function. This novel method is demonstrated with potential to conduct telehealth rehabilitation evaluations and assessments with accuracy and availability.

Keywords

Humans, Aged, Motion Capture, Bionics, Recovery of Function, Disability Evaluation, Stroke, Stroke Rehabilitation, Upper Extremity, Stroke Rehabilitation, deep learning, telemedicine

Comments

PMID: 37592867