{"id":"W4312817501","doi":"10.1109/icjece.2022.3199227","title":"Deep Incremental Learning for Personalized Human Activity Recognition on Edge Devices","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Deep learning; Activity recognition; Machine learning; Artificial intelligence; Software deployment; Wearable computer; Enhanced Data Rates for GSM Evolution; Edge computing; Wearable technology; Human–computer interaction; Embedded system; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003903104,0.0006955042,0.0006676984,0.0003409198,0.0001534592,0.000388998,0.001277362,0.000444807,0.001190641],"category_scores_gemma":[0.001475726,0.0002879562,0.0004489308,0.0005177726,0.0002298216,0.0008295845,0.0008723409,0.001056631,0.0005672092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003476902,"about_ca_system_score_gemma":0.0003702731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003879124,"about_ca_topic_score_gemma":0.009029615,"domain_scores_codex":[0.9997037,0.00005756812,0.0000147384,0.0001119047,0.00006052104,0.0000515108],"domain_scores_gemma":[0.9997136,0.0001103084,0.00002238491,0.00007993384,0.00005218476,0.00002150364],"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":[0.0004871993,0.0006172441,0.008401888,0.000126337,0.0001349805,0.0003137676,0.0001716781,0.2291091,0.01172162,0.003003184,0.01123945,0.7346735],"study_design_scores_gemma":[0.000008939166,0.00006528235,0.001460542,0.000007681521,0.00001610504,0.0000595735,0.00002006098,0.9912708,0.003196216,0.002635722,0.001251323,0.000007829844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1220273,0.0009180599,0.868577,0.0004311914,0.0001715816,0.0001001388,0.0009839567,0.004098815,0.002691932],"genre_scores_gemma":[0.888734,0.0004100045,0.1038826,0.0003747539,0.00006608567,0.0001638315,0.002021966,0.00008206302,0.004264647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003879124,"threshold_uncertainty_score":0.007713079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350932259401178,"score_gpt":0.2198667576536786,"score_spread":0.1963574350596668,"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."}}