aBnormal motION capture In aCute Stroke (BIONICS): A Low-Cost Tele-Evaluation Tool for Automated Assessment of Upper Extremity Function in Stroke Patients
Publication Date
9-1-2023
Document Type
Article
Publication Title
Neurorehabilitation and Neural Repair
Volume
37
Issue
9
DOI
10.1177/15459683231184186
First Page
591
Last Page
602
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.
Funding Number
2124789
Funding Sponsor
National Science Foundation
Keywords
deep learning, stroke rehabilitation, telemedicine
Department
Applied Data Science
Recommended Citation
Syed A. Zamin, Kaichen Tang, Emily A. Stevens, Melissa Howard, Dorothea M. Parker, Allyson Seals, Xiaoqian Jiang, Sean Savitz, and Shayan Shams. "aBnormal motION capture In aCute Stroke (BIONICS): A Low-Cost Tele-Evaluation Tool for Automated Assessment of Upper Extremity Function in Stroke Patients" Neurorehabilitation and Neural Repair (2023): 591-602. https://doi.org/10.1177/15459683231184186