{"id":"W3015014633","doi":"10.1109/jiot.2020.2985082","title":"Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":539,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Hunan Province","keywords":"Computer science; Deep learning; Artificial intelligence; Wearable computer; Big data; Activity recognition; Wearable technology; Machine learning; Cloud computing; Ubiquitous computing; The Internet; Mobile computing; Mobile device; Sensor fusion; Context (archaeology); Human–computer interaction; Data mining; World Wide Web; Embedded system; Computer network","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":[0.0003866605,0.0004038229,0.0004571769,0.0003379098,0.0001297632,0.0003018982,0.0005166612,0.0004160082,0.0008093404],"category_scores_gemma":[0.0007389343,0.0001438548,0.0003862484,0.0004413695,0.0001719089,0.0004763842,0.0005784103,0.0006121857,0.0003175615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003504419,"about_ca_system_score_gemma":0.0004262318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003014314,"about_ca_topic_score_gemma":0.004211052,"domain_scores_codex":[0.9998164,0.00004108186,0.00001327204,0.00004999646,0.00004583944,0.00003352326],"domain_scores_gemma":[0.999874,0.00003969391,0.00001704736,0.00001634449,0.00003854625,0.00001439883],"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":[0.0002806093,0.0004111524,0.0054601,0.0001789048,0.0001227749,0.0002320876,0.0001142656,0.1758065,0.02658537,0.003971809,0.007538301,0.7792981],"study_design_scores_gemma":[0.000006126251,0.00005712895,0.001275164,0.00000788528,0.00001082738,0.00005255513,0.00001615808,0.9914672,0.003931187,0.002157813,0.001011183,0.000006771216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05073649,0.001077243,0.9432629,0.000429311,0.0001764709,0.0000776185,0.0002482267,0.001539731,0.002451971],"genre_scores_gemma":[0.8636526,0.0007437794,0.1308966,0.0004569379,0.00008879477,0.0001293133,0.0007112221,0.00005276069,0.003268007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003014314,"threshold_uncertainty_score":0.005993545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05812137957742441,"score_gpt":0.3010272481516337,"score_spread":0.2429058685742093,"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."}}