{"id":"W3106359073","doi":"10.3390/s20216377","title":"Classification of Aggressive Movements Using Smartwatches","year":2020,"lang":"en","type":"letter","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Random forest; Naive Bayes classifier; Machine learning; Computer science; Smartwatch; Support vector machine; Sensitivity (control systems); Multilayer perceptron; Decision tree; Feature selection; Pattern recognition (psychology); Artificial neural network; Wearable computer; 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":[0.0003042731,0.0007215828,0.0005594645,0.001056868,0.0001287994,0.0004004441,0.0003224992,0.0004042681,0.001911055],"category_scores_gemma":[0.001168954,0.0001439022,0.0004603829,0.0005810127,0.0001495187,0.0003421323,0.0003002878,0.0002358195,0.001630476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001523888,"about_ca_system_score_gemma":0.0001469579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001726498,"about_ca_topic_score_gemma":0.00485694,"domain_scores_codex":[0.9996978,0.00005445488,0.00002480729,0.00009403047,0.00009401648,0.00003496941],"domain_scores_gemma":[0.9995317,0.0001573711,0.00009448936,0.00003968403,0.0001412224,0.00003549077],"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":[0.001424983,0.000539801,0.213053,0.0006181004,0.0002902922,0.0007550675,0.0005162857,0.02383596,0.114821,0.0006783088,0.008406411,0.6350608],"study_design_scores_gemma":[0.00008091814,0.001867263,0.5656697,0.0001666372,0.0001483039,0.001121601,0.000888801,0.3704117,0.04842897,0.001451868,0.009648502,0.0001158412],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8695765,0.0004666158,0.115173,0.000170739,0.0001678062,0.0004451363,0.004311325,0.003264708,0.006424156],"genre_scores_gemma":[0.9428611,0.0002922963,0.04713442,0.00009333366,0.00003845772,0.0003348016,0.003727874,0.00006115346,0.005456544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001911055,"threshold_uncertainty_score":0.006393075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08680730585647801,"score_gpt":0.2816580196475759,"score_spread":0.1948507137910979,"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."}}