{"id":"W4210501891","doi":"10.1109/chase52844.2021.00019","title":"Sensor-Based Human Activity Recognition for Elderly In-patients with a Luong Self-Attention Network","year":2021,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; McMaster University","funders":"","keywords":"Activity recognition; Pooling; Wearable computer; Inertial measurement unit; Computer science; Task (project management); Deep learning; Physical activity; Baseline (sea); Wearable technology; Sarcopenia; Physical medicine and rehabilitation; Artificial intelligence; Machine learning; Medicine; Embedded system; Engineering","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":[0.0003408637,0.0006624843,0.000588008,0.0004405139,0.000124258,0.0002425141,0.0006236694,0.0005071636,0.0007346503],"category_scores_gemma":[0.000780533,0.0001526577,0.0004226906,0.0003510135,0.0001271304,0.000389323,0.0004755992,0.0005517732,0.0003140365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003790464,"about_ca_system_score_gemma":0.0003234834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007594214,"about_ca_topic_score_gemma":0.01185467,"domain_scores_codex":[0.9998437,0.00003267297,0.00001174914,0.00005561328,0.00002331561,0.00003280401],"domain_scores_gemma":[0.9998442,0.0000622786,0.00002246151,0.00001303206,0.0000363051,0.00002169447],"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":[0.001297132,0.001205453,0.06249036,0.0002891154,0.0003673778,0.0006665784,0.0002300854,0.1290005,0.01580602,0.0008732853,0.01430856,0.7734656],"study_design_scores_gemma":[0.00001571059,0.0002315608,0.01921564,0.00001891965,0.00006400381,0.000125373,0.00005213894,0.9751602,0.002737032,0.0009576636,0.001410139,0.0000115171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7671836,0.007760462,0.2141421,0.001668726,0.0006566722,0.0001322898,0.002596041,0.001696007,0.004164153],"genre_scores_gemma":[0.9833609,0.0008992936,0.01177902,0.0002514845,0.0001458,0.00005564802,0.001307876,0.00001386374,0.00218602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007594214,"threshold_uncertainty_score":0.01510006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377874415358329,"score_gpt":0.2440892304318102,"score_spread":0.2203104862782269,"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."}}