{"id":"W2946689049","doi":"10.1007/s10044-020-00901-9","title":"Spatio-temporal adversarial learning for detecting unseen falls","year":2020,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute; University Health Network","funders":"","keywords":"Autoencoder; Adversarial system; Classifier (UML); Deep learning; Rendering (computer graphics); Modalities; Class (philosophy); Adversarial machine learning; Feature 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":[0.001098478,0.0009768463,0.001087601,0.0007266063,0.0003178531,0.0005437958,0.001610018,0.00110057,0.001327516],"category_scores_gemma":[0.002397625,0.0004561376,0.0007971689,0.000742586,0.0007141951,0.0007608196,0.001318176,0.001402186,0.0005617204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006238635,"about_ca_system_score_gemma":0.0007264955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006479592,"about_ca_topic_score_gemma":0.007566051,"domain_scores_codex":[0.9994884,0.0001141787,0.00002571337,0.0001664274,0.0001125323,0.00009284501],"domain_scores_gemma":[0.999131,0.0005342205,0.00007115973,0.0001070223,0.0001075714,0.00004906583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000371523,0.0002326981,0.003615273,0.00006872675,0.000121752,0.0001810218,0.00005393292,0.7715008,0.005832413,0.004361332,0.003969701,0.2096908],"study_design_scores_gemma":[0.000002019719,0.00001260524,0.0002747829,0.000002993321,0.000004826575,0.00001847519,0.000003768531,0.9980494,0.0005381877,0.0009783767,0.0001125626,0.000002161511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07672755,0.0008171426,0.918744,0.0003842729,0.0001430388,0.00005988297,0.0003612537,0.001187439,0.001575423],"genre_scores_gemma":[0.9338806,0.0003539734,0.05985037,0.000276077,0.00009819293,0.00006923312,0.0007008479,0.0000761674,0.004694482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006479592,"threshold_uncertainty_score":0.01288372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03176615309929862,"score_gpt":0.2679380683893751,"score_spread":0.2361719152900765,"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."}}