{"id":"W4318464874","doi":"10.3390/s23031412","title":"Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor Signals","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Gyroscope; Wearable computer; Dynamic time warping; Physical medicine and rehabilitation; Gait training; Rehabilitation; Gait analysis; Inertial measurement unit; Artificial intelligence; Computer science; Physical therapy; Engineering; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004962447,0.0005671162,0.0005039856,0.00139804,0.0001273558,0.0005246819,0.0002611906,0.0004676132,0.000420051],"category_scores_gemma":[0.001986823,0.0001564674,0.0003751064,0.0007762186,0.0002226241,0.0004476048,0.0003257686,0.0002308199,0.0002347971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002114539,"about_ca_system_score_gemma":0.0002104073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001821594,"about_ca_topic_score_gemma":0.002779089,"domain_scores_codex":[0.9995963,0.0000825358,0.00004593822,0.0001094993,0.0001272802,0.00003839695],"domain_scores_gemma":[0.9994795,0.0001737027,0.000129859,0.00005060282,0.0001337368,0.00003269966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00104991,0.0007492498,0.1060838,0.0003566064,0.0003802027,0.0005615538,0.0003934961,0.0812287,0.09383192,0.0004373736,0.001356313,0.7135709],"study_design_scores_gemma":[0.00003639322,0.001069235,0.2142355,0.00009477802,0.00009151269,0.0006831994,0.0003865044,0.7617409,0.01951698,0.0009342238,0.001137265,0.00007349661],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8319936,0.0006521526,0.1652055,0.0001239878,0.00007727995,0.00009516635,0.0002736861,0.0005246449,0.001054003],"genre_scores_gemma":[0.9715759,0.0002671661,0.02720144,0.00004721617,0.00002274861,0.00005199934,0.000231474,0.00001264261,0.0005894616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001821594,"threshold_uncertainty_score":0.003621936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04164637373845552,"score_gpt":0.3830581686748421,"score_spread":0.3414117949363866,"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."}}