AUTHOR=Geed Shashwati , Grainger Megan L. , Mitchell Abigail , Anderson Cassidy C. , Schmaulfuss Henrike L. , Culp Seraphina A. , McCormick Eilis R. , McGarry Maureen R. , Delgado Mystee N. , Noccioli Allysa D. , Shelepov Julia , Dromerick Alexander W. , Lum Peter S.
TITLE=Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke
JOURNAL=Frontiers in Physiology
VOLUME=14
YEAR=2023
URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2023.1116878
DOI=10.3389/fphys.2023.1116878
ISSN=1664-042X
ABSTRACT=Objective: This study aims to investigate the validity of machine learning-derived amount of real-world functional upper extremity (UE) use in individuals with stroke. We hypothesized that machine learning classification of wrist-worn accelerometry will be as accurate as frame-by-frame video labeling (ground truth). A second objective was to validate the machine learning classification against measures of impairment, function, dexterity, and self-reported UE use.
Design: Cross-sectional and convenience sampling.
Setting: Outpatient rehabilitation.
Participants: Individuals (>18 years) with neuroimaging-confirmed ischemic or hemorrhagic stroke >6-months prior (n = 31) with persistent impairment of the hemiparetic arm and upper extremity Fugl-Meyer (UEFM) score = 12–57.
Methods: Participants wore an accelerometer on each arm and were video recorded while completing an “activity script” comprising activities and instrumental activities of daily living in a simulated apartment in outpatient rehabilitation. The video was annotated to determine the ground-truth amount of functional UE use.
Main outcome measures: The amount of real-world UE use was estimated using a random forest classifier trained on the accelerometry data. UE motor function was measured with the Action Research Arm Test (ARAT), UEFM, and nine-hole peg test (9HPT). The amount of real-world UE use was measured using the Motor Activity Log (MAL).
Results: The machine learning estimated use ratio was significantly correlated with the use ratio derived from video annotation, ARAT, UEFM, 9HPT, and to a lesser extent, MAL. Bland–Altman plots showed excellent agreement between use ratios calculated from video-annotated and machine-learning classification. Factor analysis showed that machine learning use ratios capture the same construct as ARAT, UEFM, 9HPT, and MAL and explain 83% of the variance in UE motor performance.
Conclusion: Our machine learning approach provides a valid measure of functional UE use. The accuracy, validity, and small footprint of this machine learning approach makes it feasible for measurement of UE recovery in stroke rehabilitation trials.