{"id":"W2998481686","doi":"10.1299/jsmermd.2019.2a1-g03","title":"Anomaly Detection Based on Deep Learning Using Skeleton Information for Prevention of Industrial Accident","year":2019,"lang":"en","type":"article","venue":"The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Conestoga Meat Packers (Canada)","funders":"","keywords":"Autoencoder; Anomaly detection; Computer science; Accident (philosophy); Skeleton (computer programming); Artificial intelligence; Deep learning; Anomaly (physics); Pattern recognition (psychology); Machine learning","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":[0.0005446938,0.0005425096,0.0007040976,0.001583664,0.0002869115,0.0002840417,0.0006741369,0.0005168523,0.000698435],"category_scores_gemma":[0.001449317,0.0002744149,0.0004525252,0.0005656704,0.000315643,0.0006413693,0.0006585157,0.0005812831,0.000222843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004461986,"about_ca_system_score_gemma":0.0006675371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004208654,"about_ca_topic_score_gemma":0.004680467,"domain_scores_codex":[0.999477,0.00006607007,0.00003454402,0.0001443918,0.0001906906,0.00008746087],"domain_scores_gemma":[0.9993235,0.0001705645,0.0001211262,0.0000519422,0.0002821269,0.00005079585],"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":[0.0003381019,0.0003664897,0.02660503,0.0001018402,0.0001029206,0.0004045983,0.000122408,0.1439959,0.03524471,0.001566209,0.002612457,0.7885393],"study_design_scores_gemma":[0.000004958114,0.00006473866,0.005602846,0.000007993131,0.0000213583,0.0001141794,0.00001589771,0.9840572,0.008765546,0.0009716593,0.0003635746,0.000009993568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2796028,0.0006961843,0.7149224,0.0002780397,0.0001005281,0.00005153228,0.0001528229,0.00263627,0.001559513],"genre_scores_gemma":[0.9287488,0.0002513231,0.06927964,0.00005467961,0.00002961167,0.00002054322,0.0002084454,0.00003487863,0.001372086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004208654,"threshold_uncertainty_score":0.008368313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02517907245718351,"score_gpt":0.2582045130124924,"score_spread":0.2330254405553089,"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."}}