{"id":"W3210766530","doi":"10.3390/s22041476","title":"Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances","year":2022,"lang":"en","type":"review","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":489,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Wearable computer; Activity recognition; Deep learning; Human–computer interaction; Wearable technology; Computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009649626,0.001081127,0.0009180286,0.001574631,0.0001931036,0.0009431981,0.001033179,0.001165774,0.003079372],"category_scores_gemma":[0.001830992,0.0004291649,0.0005857928,0.002631332,0.000494099,0.0019234,0.0008057737,0.001668895,0.001858933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005334312,"about_ca_system_score_gemma":0.001224988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001989012,"about_ca_topic_score_gemma":0.001745922,"domain_scores_codex":[0.9997032,0.00005483649,0.00003376336,0.00007261628,0.0001116624,0.0000239033],"domain_scores_gemma":[0.9990446,0.000608401,0.00006014901,0.00002687379,0.0002231168,0.00003691867],"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":[0.0000347416,0.00007205294,0.0003830842,0.007401881,0.0000789863,0.00005412024,0.00005350937,0.002014118,0.000708158,0.008677985,0.01870109,0.9618204],"study_design_scores_gemma":[0.00001668583,0.0002248989,0.001657849,0.007835197,0.0002630609,0.00061972,0.000113601,0.006628578,0.002267179,0.01587832,0.9644268,0.00006817307],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000453224,0.9893756,0.006102656,0.0007987441,0.0003635731,0.00001312832,0.00005642632,0.00004250732,0.002794166],"genre_scores_gemma":[0.003433286,0.9920854,0.002609253,0.0003295108,0.0003592646,0.00001643118,0.00008600787,0.00001071695,0.00107004],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003079372,"threshold_uncertainty_score":0.01030159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07817396725530723,"score_gpt":0.3296617954657807,"score_spread":0.2514878282104734,"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."}}