{"doi":"10.3389/fphys.2023.1116878","title":"Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke","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.","journal":"Frontiers in Physiology","year":2023,"id":340854,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.944,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1076363,"name":"Megan L. Grainger","orcid":null,"position":1,"is_corresponding":false},{"id":620397,"name":"Abigail Mitchell","orcid":"0000-0003-1951-0660","position":2,"is_corresponding":false},{"id":1032637,"name":"Cassidy C. Anderson","orcid":null,"position":3,"is_corresponding":false},{"id":1076364,"name":"Henrike L. Schmaulfuss","orcid":null,"position":4,"is_corresponding":false},{"id":1076365,"name":"Seraphina A. Culp","orcid":null,"position":5,"is_corresponding":false},{"id":1076366,"name":"Eilis R. McCormick","orcid":null,"position":6,"is_corresponding":false},{"id":1076367,"name":"Maureen R. McGarry","orcid":null,"position":7,"is_corresponding":false},{"id":1076368,"name":"Mystee N. Delgado","orcid":null,"position":8,"is_corresponding":false},{"id":1076369,"name":"Allysa D. Noccioli","orcid":null,"position":9,"is_corresponding":false},{"id":1076370,"name":"Julia Shelepov","orcid":null,"position":10,"is_corresponding":false},{"id":239912,"name":"Alexander W. Dromerick","orcid":"0000-0001-5777-4531","position":11,"is_corresponding":false},{"id":551729,"name":"Peter S. Lum","orcid":"0000-0002-4735-6114","position":12,"is_corresponding":false},{"id":620395,"name":"Shashwati Geed","orcid":"0000-0003-0190-6923","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T01:10:58.548803Z","pmid":"37035665","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}