{"id":"W4280630662","doi":"10.21203/rs.3.rs-1614908/v2","title":"Attention Vision Transformers for Human Fall Detection","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Transformer; Inertial measurement unit; Computer science; Artificial intelligence; Accelerometer; Machine translation; Activity recognition; Sentence; Computer vision; Pattern recognition (psychology); Speech recognition; Simulation; Engineering; Voltage","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":[0.0006461208,0.001031423,0.0007269547,0.0009823986,0.0001842965,0.0005658916,0.001029019,0.0006115003,0.002745498],"category_scores_gemma":[0.001896456,0.0002237933,0.0005935562,0.0006050951,0.0003764634,0.0006217379,0.0005532174,0.0007561287,0.0008846688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009679336,"about_ca_system_score_gemma":0.0006208621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01489564,"about_ca_topic_score_gemma":0.01022481,"domain_scores_codex":[0.9996449,0.00005955232,0.00001308328,0.0001365374,0.00008251179,0.00006343351],"domain_scores_gemma":[0.9995986,0.0001489097,0.00003908664,0.000039953,0.000141987,0.00003142965],"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":[0.0009699996,0.000465535,0.007521745,0.0001639794,0.0001751247,0.0002075431,0.00006751904,0.1518469,0.03060285,0.001619686,0.008573305,0.7977859],"study_design_scores_gemma":[0.00001489199,0.0001791204,0.003181977,0.000009240651,0.00003452977,0.00008304571,0.00001263507,0.9885852,0.006071115,0.001248347,0.000572666,0.000007299465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2967162,0.003715728,0.683684,0.0007831917,0.000322997,0.0002493582,0.0006100527,0.007883556,0.006034927],"genre_scores_gemma":[0.9737517,0.000276201,0.02354208,0.0001536131,0.00006655657,0.00002815321,0.000269838,0.00003982503,0.001872095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01489564,"threshold_uncertainty_score":0.02961791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1209262664547508,"score_gpt":0.4400089321862193,"score_spread":0.3190826657314685,"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."}}