{"id":"W4390493352","doi":"10.1093/ptj/pzad172","title":"Wearable Technology to Capture Arm Use of People With Stroke in Home and Community Settings: Feasibility and Early Insights on Motor Performance","year":2024,"lang":"en","type":"article","venue":"Physical Therapy","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"National Center for Medical Rehabilitation Research; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Usability; Wearable computer; Physical medicine and rehabilitation; System usability scale; Activities of daily living; Physical therapy; Rehabilitation; Medicine; Wearable technology; Psychology; Computer science; Human–computer interaction; Web usability; Embedded system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008770284,0.00009472236,0.0002591695,0.0001535972,0.00004005747,0.00001568746,0.00003317583,0.0000528402,0.000002394136],"category_scores_gemma":[0.00003549938,0.00005971266,0.00002789238,0.0002748394,0.0001114166,0.00008714293,0.00001744094,0.0004181149,0.00000169567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003246601,"about_ca_system_score_gemma":0.00002413061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001072293,"about_ca_topic_score_gemma":0.00001598257,"domain_scores_codex":[0.9994781,0.00006150611,0.00009507493,0.0001544863,0.00010969,0.0001011293],"domain_scores_gemma":[0.9993756,0.0003221036,0.00001751107,0.0001940521,0.00003701308,0.00005365745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002587547,0.001647322,0.867468,0.0006179346,0.0001013028,0.000004818577,0.0233187,0.00002490185,0.07378612,0.0004036484,0.00002474081,0.03001497],"study_design_scores_gemma":[0.001002705,0.004893198,0.9866367,0.000492714,0.000008193735,0.000003807061,0.0007336984,0.0004601534,0.004958697,0.0003530489,0.0003705765,0.00008651457],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981169,0.0006344394,0.000005370245,0.0006832862,0.00002555487,0.0004343932,0.000008093397,0.00003811299,0.00005383866],"genre_scores_gemma":[0.9992324,0.0003156742,0.0001887171,0.0001162676,0.0000214805,0.00002164993,7.467583e-7,0.00001030661,0.00009281913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1191687,"threshold_uncertainty_score":0.2435012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02064225157723313,"score_gpt":0.2827943400360271,"score_spread":0.262152088458794,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}