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

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.

Characterization of Bilateral Reaching Development Using Augmented Reality Games

“…Computer vision algorithms offer the opportunity to use readily available video, such as from a smartphone or webcam, to estimate pose information, or where the limbs are in space, and derive kinematic variables (Kidziński et al, 2020; Stenum et al, 2021). Computer vision has been used mostly for the analysis of gait, but has significant potential with upper limb movements as well (Cóias et al, 2022; Zamin et al, 2023). While the accuracy of computer vision approaches varies based on many factors including camera setup, plane of movement, and clothing, pose estimation algorithms have been shown to be valid, reliable, and accurate for many human motion analysis scenarios (Dill et.…”