{"id":"W3203871918","doi":"10.1145/3472290","title":"A Survey on Deep Learning for Human Activity Recognition","year":2021,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":253,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Deep learning; Computer science; Artificial intelligence; Data science; Activity recognition; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01403165,0.0008788339,0.002929665,0.000533348,0.0008235013,0.0008868159,0.002310602,0.0005839012,0.00001976754],"category_scores_gemma":[0.006811941,0.0009186741,0.001032518,0.001412801,0.00005171814,0.0003743487,0.001382557,0.001335273,0.0002553162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003768828,"about_ca_system_score_gemma":0.0003783375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008060866,"about_ca_topic_score_gemma":0.0008890128,"domain_scores_codex":[0.979427,0.01614924,0.001070299,0.001917803,0.0006115048,0.0008241739],"domain_scores_gemma":[0.9762945,0.01928117,0.001479568,0.001935159,0.0008069573,0.0002026014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001384204,0.0001364236,0.00008050286,0.001771036,0.0002037004,0.00001487378,0.00006616951,0.000007109614,7.651463e-7,0.00001743812,0.0002480059,0.9974526],"study_design_scores_gemma":[0.002327464,0.001386168,0.01270153,0.05950325,0.0006390487,0.000282417,0.00002936288,0.01142713,0.00004500259,0.0006407649,0.9042533,0.006764592],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0001859424,0.462945,0.5329041,0.0000172909,0.001627596,0.001398774,0.00008951399,0.0006234813,0.0002082597],"genre_scores_gemma":[0.04934673,0.9296207,0.008976474,0.0001710541,0.002337062,0.000598641,0.007302623,0.0006093517,0.001037325],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.990688,"threshold_uncertainty_score":0.9993264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2303350838921676,"score_gpt":0.3915422512201308,"score_spread":0.1612071673279632,"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."}}