TY  - BOOK
AU  - Anderson, Cassidy C
AU  - Dromerick, Alexander W
AU  - Geed, Shashwati
AU  - Grainger, Megan L
AU  - Lum, Peter S
AU  - Mitchell, Abigail
TI  - Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke
SN  - 1664-042X
PY  - 2023///
KW  - Automated
KW  - MedStar National Rehabilitation Network
KW  - Journal Article
N2  - 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. Copyright © 2023 Geed, Grainger, Mitchell, Anderson, Schmaulfuss, Culp, McCormick, McGarry, Delgado, Noccioli, Shelepov, Dromerick and Lum
UR  - https://dx.doi.org/10.3389/fphys.2023.1116878
ER  -