{"id":"W3177026772","doi":"10.1016/j.mlwa.2021.100072","title":"Trends in human activity recognition with focus on machine learning and power requirements","year":2021,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Activity recognition; Convolutional neural network; Data science; Field (mathematics); Deep learning; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003646566,0.0002548624,0.0002900357,0.000383208,0.0004782907,0.0002297844,0.0002077947,0.00007417294,0.00009475376],"category_scores_gemma":[0.00003973439,0.0002281362,0.00003973455,0.001217812,0.00005610762,0.0004368001,0.0001428887,0.0007808295,0.00003637433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000917,"about_ca_system_score_gemma":0.00004671545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004593899,"about_ca_topic_score_gemma":0.001660642,"domain_scores_codex":[0.9979405,0.0003625509,0.0002376129,0.0007910149,0.0003600073,0.0003082994],"domain_scores_gemma":[0.9988852,0.0001980894,0.0002485643,0.0004051942,0.0001415951,0.0001213955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009524704,0.000565099,0.1499023,0.00003150704,0.00008303403,0.00005489483,0.0007821957,0.0003197878,0.005346217,0.001236975,0.00001779593,0.841565],"study_design_scores_gemma":[0.02331179,0.008170246,0.6779026,0.001914692,0.0002908178,0.00188236,0.0009162747,0.1314611,0.03450847,0.004997985,0.1087516,0.005892134],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4813742,0.0002378224,0.489787,0.003485474,0.00004731967,0.0008122982,0.00002724446,0.0007485113,0.02348012],"genre_scores_gemma":[0.9952039,0.00001030074,0.002911039,0.00007336479,0.00002919933,0.0003618311,0.0001096868,0.00003352988,0.00126716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8356728,"threshold_uncertainty_score":0.9303123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02747832761359099,"score_gpt":0.2885644445983348,"score_spread":0.2610861169847438,"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."}}