{"id":"W2007836338","doi":"10.1007/s11517-011-0736-0","title":"Quasi real-time gait event detection using shank-attached gyroscopes","year":2011,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Gyroscope; Gait; Accelerometer; Robustness (evolution); Computer science; Event (particle physics); Artificial intelligence; Acceleration; Step detection; Computer vision; Gait analysis; Algorithm; Engineering; Physical medicine and rehabilitation; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000242059,0.0004324284,0.0005310959,0.0006492126,0.0001884065,0.000405147,0.0002841114,0.0004508127,0.001834031],"category_scores_gemma":[0.0009097661,0.0002014599,0.0001646787,0.0005569285,0.0002499393,0.0003990261,0.0003430178,0.0002418793,0.0006806049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001157965,"about_ca_system_score_gemma":0.000304276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001021204,"about_ca_topic_score_gemma":0.002000536,"domain_scores_codex":[0.9996972,0.00005475433,0.00001411994,0.000085417,0.0001166387,0.00003196766],"domain_scores_gemma":[0.9996761,0.00007118734,0.00004924866,0.00005054244,0.0001116013,0.00004146884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002350295,0.0002821459,0.03013889,0.0005296586,0.0001518191,0.000365822,0.0003824532,0.01112137,0.4799351,0.001279954,0.003958469,0.469504],"study_design_scores_gemma":[0.0002680833,0.002404232,0.4355435,0.0001145056,0.0002151365,0.002891107,0.0003335753,0.400541,0.146312,0.002723072,0.008499605,0.0001540958],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4682951,0.0007055986,0.523369,0.0001314465,0.0003368476,0.0001930696,0.001302628,0.002570773,0.003095562],"genre_scores_gemma":[0.9269021,0.0002065672,0.07069409,0.00007032492,0.0000816853,0.00007631747,0.0004341028,0.000054199,0.001480638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001834031,"threshold_uncertainty_score":0.006135464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02974547565582373,"score_gpt":0.2437646585279827,"score_spread":0.214019182872159,"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."}}