{"id":"W2035675395","doi":"10.1109/embc.2014.6943521","title":"The effect of window size and lead time on pre-impact fall detection accuracy using support vector machine analysis of waist mounted inertial sensor data","year":2014,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Accelerometer; Wearable computer; Gyroscope; Support vector machine; Inertial measurement unit; Computer science; Waist; Real-time computing; Artificial intelligence; Sensitivity (control systems); Event (particle physics); Simulation; Engineering; Medicine; Embedded system","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":[0.004976524,0.0007207398,0.000764763,0.0008484942,0.0002526587,0.0007704969,0.0003246744,0.0005268363,0.0004334497],"category_scores_gemma":[0.02778548,0.0002297554,0.000562288,0.000519837,0.0002703255,0.001071663,0.0004155855,0.0006525817,0.0002177407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001957555,"about_ca_system_score_gemma":0.0003249259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002428005,"about_ca_topic_score_gemma":0.001594906,"domain_scores_codex":[0.9982466,0.0004347033,0.0003659402,0.0003741322,0.0003976118,0.0001810776],"domain_scores_gemma":[0.9704307,0.02541978,0.001154086,0.001008873,0.001599689,0.0003868981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01474616,0.001176196,0.4223932,0.0003562347,0.0007245832,0.0004103867,0.0004844131,0.07529657,0.07902981,0.0001994575,0.0011662,0.4040168],"study_design_scores_gemma":[0.000122818,0.00702436,0.3842541,0.00007600785,0.0005132186,0.0006133122,0.0003253497,0.5272201,0.07886571,0.0002902876,0.0005884184,0.0001063169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868792,0.0006524785,0.01167965,0.00006623848,0.00008314246,0.00003112409,0.0001210328,0.0002304204,0.0002567887],"genre_scores_gemma":[0.9928645,0.0001613751,0.006540453,0.00002079268,0.00001889043,0.00001575254,0.0002096684,0.00002510301,0.0001434185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004976524,"threshold_uncertainty_score":0.02631867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0206745563689059,"score_gpt":0.3100693474094995,"score_spread":0.2893947910405936,"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."}}