{"id":"W4309344223","doi":"10.1109/smc53654.2022.9945513","title":"Multimodal Human Activity Recognition for Smart Healthcare Applications","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Activity recognition; Modalities; Convolutional neural network; Computer science; Wearable computer; Robustness (evolution); Sensor fusion; Artificial intelligence; Deep learning; Assisted living; Machine learning; Human–computer interaction; 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":[0.0005429151,0.0007019259,0.000599619,0.0006514998,0.0001032537,0.0004321907,0.0004763544,0.0006104538,0.002888859],"category_scores_gemma":[0.001069404,0.0001336173,0.0004264273,0.000566198,0.0001473026,0.0006003607,0.0006728701,0.000504554,0.001178129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000248078,"about_ca_system_score_gemma":0.0002539621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009772885,"about_ca_topic_score_gemma":0.001555656,"domain_scores_codex":[0.9996886,0.00008069439,0.00002670748,0.00008841406,0.00007728354,0.00003826112],"domain_scores_gemma":[0.9998029,0.00005691296,0.00002951018,0.0000323922,0.00006187524,0.00001642933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006041813,0.0002689971,0.005017035,0.0003663035,0.0001436727,0.0002444099,0.00009467403,0.01565846,0.06049842,0.001423917,0.009930951,0.9057489],"study_design_scores_gemma":[0.00007365694,0.0007397619,0.02991456,0.0001974413,0.0002271508,0.001236357,0.0002741609,0.8404728,0.08889847,0.01229678,0.02558026,0.00008873308],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07519379,0.006376415,0.9023544,0.0008858696,0.000379202,0.0002425699,0.001703186,0.005519549,0.007345076],"genre_scores_gemma":[0.8294284,0.002938046,0.1598527,0.0007470488,0.0002223186,0.0002528703,0.002379674,0.00009756858,0.004081476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002888859,"threshold_uncertainty_score":0.009664178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1138413629557532,"score_gpt":0.3348314930241096,"score_spread":0.2209901300683564,"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."}}