{"id":"W4402187468","doi":"10.1109/abc61795.2024.10651644","title":"Daily Routine Recognition from Longitudinal, Real-Life Wearable Sensor Data for the Elderly","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Wearable computer; Computer science; Wearable technology; Embedded system","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":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009701818,0.0001998443,0.0002401799,0.00009418127,0.0002498469,0.001247719,0.001409226,0.00008844885,0.0002456694],"category_scores_gemma":[0.0002804705,0.000141227,0.0001029145,0.0004410183,0.00003985842,0.001831873,0.0004922645,0.0001884568,0.001017604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004691709,"about_ca_system_score_gemma":0.0001842545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00317184,"about_ca_topic_score_gemma":0.00080552,"domain_scores_codex":[0.9978714,0.0001245609,0.0003768304,0.0009608892,0.0003417114,0.000324598],"domain_scores_gemma":[0.995374,0.002470165,0.00008764895,0.001770895,0.0001825477,0.0001147299],"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":[0.00004440513,0.00009952918,0.0003141247,0.00009176295,0.0004155735,0.00003136588,0.0004823676,0.00000888726,0.0009016587,0.001543943,0.1296878,0.8663785],"study_design_scores_gemma":[0.00117613,0.000350198,0.003185999,0.0005163081,0.0002057983,0.00009316099,0.0007547891,0.8411562,0.001510006,0.005301916,0.1449214,0.0008280682],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01470609,0.001098886,0.9683501,0.00895585,0.001800558,0.0007206738,0.000592226,0.0008067958,0.002968782],"genre_scores_gemma":[0.9824931,0.0002344541,0.01230618,0.0004349172,0.0008321356,0.0001371855,0.0002766165,0.00003293635,0.003252457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.967787,"threshold_uncertainty_score":0.9997891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1451450658040928,"score_gpt":0.3196319762504172,"score_spread":0.1744869104463244,"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."}}