{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001322367,0.0007030525,0.002297933,0.0006397294,0.0003559589,0.0001709034,0.0007589618,0.0002015394,0.0003047023],"category_scores_gemma":[0.0003008955,0.0006128045,0.0004327427,0.001838341,0.00005693989,0.0006832283,0.0002422012,0.001850459,0.0004494555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005454246,"about_ca_system_score_gemma":0.0002029612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001205388,"about_ca_topic_score_gemma":0.0001935951,"domain_scores_codex":[0.993569,0.002882036,0.0007642828,0.001378399,0.0008166842,0.0005896261],"domain_scores_gemma":[0.9965739,0.001168279,0.001109247,0.000915663,0.00009472675,0.0001381326],"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":[0.000006310783,0.0000969414,0.000001988942,0.01422549,0.00004865474,0.0001378695,0.00008749111,0.00005880992,3.397197e-7,0.000006920631,0.0000302848,0.9852989],"study_design_scores_gemma":[0.0002418237,0.0003191885,0.000002812523,0.0857676,0.0001187856,0.000304014,0.00003903125,0.0003089269,0.000003867171,0.0000293314,0.9121137,0.000750914],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001280963,0.990863,0.000426279,0.00007523064,0.0002734114,0.001886855,0.00001101632,0.0003879552,0.00594812],"genre_scores_gemma":[0.0001473964,0.9978709,0.0002405111,0.00009743396,0.000105243,0.0005491613,0.00004268188,0.00008031067,0.0008663653],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.984548,"threshold_uncertainty_score":0.9996324,"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."}}