{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007150472,0.0002833406,0.0003572898,0.0003242246,0.0007201894,0.0004760539,0.0009172515,0.0001001831,0.0001231931],"category_scores_gemma":[0.00002727936,0.0003295414,0.0001231021,0.0002324295,0.00006362078,0.0003629102,0.0002830545,0.0004036765,0.0000632302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002904633,"about_ca_system_score_gemma":0.000159532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001171084,"about_ca_topic_score_gemma":0.0004402879,"domain_scores_codex":[0.9970731,0.0003571846,0.0005263464,0.0008655372,0.0008410558,0.0003367845],"domain_scores_gemma":[0.9979822,0.0002914559,0.0004625095,0.0005131478,0.0005840583,0.0001666271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000433753,0.002053792,0.003984689,0.0007271081,0.0006793553,0.00004425396,0.003740655,0.000312812,0.0204815,0.5808215,0.01124778,0.3754728],"study_design_scores_gemma":[0.008999851,0.003358079,0.009818907,0.0006506027,0.000153788,0.0007567825,0.006405264,0.4968469,0.004511528,0.04537359,0.4191611,0.003963558],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3417383,0.0004480987,0.5915523,0.01127596,0.01117302,0.009350258,0.004482999,0.001071177,0.02890785],"genre_scores_gemma":[0.9922442,0.00003679784,0.0003290771,0.0003672931,0.0003411339,0.003369372,0.0002285428,0.0000289805,0.003054538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6505059,"threshold_uncertainty_score":0.9999157,"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."}}