{"id":"W4407369415","doi":"10.3390/s25041083","title":"High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Sports Performance and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Squatting position; Inertial measurement unit; Dynamic time warping; Artificial intelligence; Computer science; Gait; Activity recognition; Knee flexion; STRIDE; Scale (ratio); Gait analysis; Accelerometer; Computer vision; Physical medicine and rehabilitation; Pattern recognition (psychology); Physical therapy; Medicine; Geography","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":[0.0004448805,0.0005670678,0.0004114507,0.0008072554,0.0001224735,0.0004927595,0.0003354856,0.0003025326,0.0007893983],"category_scores_gemma":[0.001534503,0.0001861716,0.0004467086,0.0007301825,0.0001776821,0.0004859966,0.0004431595,0.0003161426,0.0005876721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001473664,"about_ca_system_score_gemma":0.0002378903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001581074,"about_ca_topic_score_gemma":0.002860435,"domain_scores_codex":[0.9996908,0.0000488322,0.00002490281,0.0001160088,0.00009157688,0.00002792471],"domain_scores_gemma":[0.9996499,0.0001090234,0.00007736461,0.00005071595,0.00009541089,0.00001756053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004923707,0.0003747504,0.03845209,0.0004227129,0.000227328,0.0004354812,0.0003821301,0.0909938,0.143923,0.0009219981,0.001042096,0.7223323],"study_design_scores_gemma":[0.00001736396,0.0004714387,0.1141538,0.0000583396,0.0000573841,0.0007696712,0.0001798771,0.8477879,0.03345338,0.001396397,0.001603944,0.00005061048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2463014,0.0002877507,0.7513388,0.00004337882,0.00004739095,0.0001094584,0.000383693,0.0005418271,0.0009461897],"genre_scores_gemma":[0.8555106,0.0003073953,0.1424856,0.00003148114,0.00002323943,0.0001373767,0.0005291682,0.00003740893,0.0009377677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001581074,"threshold_uncertainty_score":0.003143728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05168691636735369,"score_gpt":0.3308532742620001,"score_spread":0.2791663578946464,"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."}}