{"id":"W2604425430","doi":"10.1186/s12984-017-0241-2","title":"Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson’s disease using multiple inertial sensors","year":2017,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke; Institut Universitaire de Gériatrie de Montréal; Université du Québec à Montréal","funders":"Canadian Institutes of Health Research; Université du Québec à Montréal; University of Windsor","keywords":"Inertial measurement unit; Wearable computer; Segmentation; Artificial intelligence; Computer science; Physical medicine and rehabilitation; Population; Task (project management); Sitting; Step detection; Activities of daily living; Machine learning; Computer vision; Medicine; Physical therapy; Engineering","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":[0.0003403909,0.0004307383,0.0003266092,0.0009451021,0.0001446333,0.0003158986,0.0001624611,0.0004219667,0.0003818146],"category_scores_gemma":[0.001462374,0.0001353369,0.0002253091,0.0003760883,0.0001418742,0.000286969,0.0003303564,0.000127414,0.0001533546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001230346,"about_ca_system_score_gemma":0.000119375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001620228,"about_ca_topic_score_gemma":0.004069351,"domain_scores_codex":[0.9998193,0.00003814621,0.00002513699,0.00005226379,0.00004614349,0.00001904263],"domain_scores_gemma":[0.9995407,0.0001538164,0.0001327341,0.00002542871,0.0001011183,0.00004617839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003037683,0.0004298124,0.700347,0.0004274915,0.0002172494,0.0007842585,0.001658717,0.002834565,0.07170735,0.00006858832,0.0005368281,0.2179504],"study_design_scores_gemma":[0.00003821268,0.0009858409,0.9780368,0.00002406755,0.00009035446,0.0009878111,0.0004775975,0.01165731,0.007260095,0.0001275011,0.0002888901,0.00002555727],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965146,0.0001621008,0.002955695,0.00001455082,0.000005327965,0.00002681003,0.0000859367,0.00002706125,0.0002078563],"genre_scores_gemma":[0.9937052,0.0001195391,0.005805441,0.00001383117,0.000009833337,0.00003305239,0.0001562608,0.000004687638,0.0001521681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001620228,"threshold_uncertainty_score":0.003221631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009735732816003862,"score_gpt":0.2808705243953871,"score_spread":0.2711347915793832,"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."}}