{"id":"W110252780","doi":"10.1007/978-3-642-21535-3_16","title":"Fall Detection from Depth Map Video Sequences","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Computer vision; Centroid; Artificial intelligence; Feature (linguistics); Occlusion","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004269595,0.0004667558,0.0003928722,0.0007343544,0.0003170947,0.000586011,0.002224322,0.000367131,0.00009833963],"category_scores_gemma":[0.00003436688,0.000433229,0.000145426,0.0003365966,0.0004378222,0.001131126,0.0006114828,0.0007545431,0.0005050736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002382174,"about_ca_system_score_gemma":0.0002735512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004295465,"about_ca_topic_score_gemma":0.002137903,"domain_scores_codex":[0.9967112,0.00004378528,0.0004682134,0.001516587,0.0007521043,0.0005081],"domain_scores_gemma":[0.9980602,0.0002516528,0.0003233724,0.000980482,0.0002140981,0.0001701454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004775318,0.00001890296,0.00003309083,0.0000137481,0.00001302906,0.00005823902,0.0005867495,0.0006029478,0.0004433755,0.002355955,0.00002032653,0.9958488],"study_design_scores_gemma":[0.0003238283,0.0002790911,0.0004638461,0.0004938323,0.00002131133,0.00006803501,3.323866e-7,0.09812658,0.02358786,0.868506,0.006987349,0.001141959],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004399659,0.000289916,0.9881691,0.0002070948,0.003290005,0.0002567602,0.000007128964,0.0002804378,0.007059596],"genre_scores_gemma":[0.7942795,0.0001159101,0.2007718,0.00241365,0.001512445,0.00002272506,0.00001986255,0.00005144538,0.0008127402],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9947069,"threshold_uncertainty_score":0.9998119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02769935014838703,"score_gpt":0.2359134526545583,"score_spread":0.2082141025061713,"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."}}