{"id":"W2901750619","doi":"10.1109/iemcon.2018.8614822","title":"Detecting Irregular Patterns in IoT Streaming Data for Fall Detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Analytics; Cloud computing; Wearable computer; IBM; Accelerometer; Data stream mining; Machine learning; Real-time computing; Artificial intelligence; Data mining; Embedded system; Operating system","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":[0.0005283442,0.0008464186,0.0005598,0.00119212,0.0003504506,0.0005615328,0.000827971,0.0004958457,0.00115302],"category_scores_gemma":[0.002925362,0.0002632669,0.0003821791,0.001384464,0.0002670715,0.0008671147,0.0006564026,0.0006704269,0.0005923841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004465677,"about_ca_system_score_gemma":0.000526053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005043583,"about_ca_topic_score_gemma":0.01280175,"domain_scores_codex":[0.9996083,0.00005120761,0.00003977524,0.0001055817,0.0001592886,0.0000358363],"domain_scores_gemma":[0.9991709,0.0002129745,0.0001379161,0.0001571817,0.0002549319,0.00006611223],"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":[0.001208941,0.001284124,0.1500858,0.0005037634,0.0002447348,0.0008748922,0.000356485,0.1457163,0.04483291,0.002636711,0.02533806,0.6269172],"study_design_scores_gemma":[0.00002377746,0.0001545827,0.02119242,0.00004066259,0.00002628729,0.000268672,0.0001371598,0.961674,0.00911551,0.004079771,0.003268716,0.00001843312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.424063,0.001138882,0.5537418,0.001765344,0.0004163258,0.000350286,0.00727854,0.00615121,0.005094683],"genre_scores_gemma":[0.858382,0.0004707635,0.1339408,0.0002120718,0.000100858,0.0001216644,0.005260745,0.000105527,0.0014056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005043583,"threshold_uncertainty_score":0.01002848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07543558258928121,"score_gpt":0.3039385975527931,"score_spread":0.2285030149635119,"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."}}