{"id":"W2901081366","doi":"10.2196/11201","title":"Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Environmental Health Sciences","keywords":"Wearable computer; Computer science; Multivariate statistics; Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer vision; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002076873,0.0002292805,0.0003805174,0.0002217809,0.0007714684,0.0002530691,0.0005482356,0.0001240188,0.00004040079],"category_scores_gemma":[0.0001083252,0.0002370882,0.00002899517,0.0005041016,0.00004888109,0.001347256,0.0004713458,0.0002742922,0.0002066002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001678179,"about_ca_system_score_gemma":0.0002148371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006604075,"about_ca_topic_score_gemma":0.0007681273,"domain_scores_codex":[0.9965044,0.00106969,0.000456767,0.001090508,0.0003224872,0.0005562069],"domain_scores_gemma":[0.9973849,0.0004516748,0.0003603593,0.001060927,0.0001807561,0.000561362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005462336,0.0001008632,0.0003561306,0.0001205729,0.00001552943,0.000001694357,0.002044681,3.946514e-7,0.01205487,0.00003649882,0.0007347378,0.9844794],"study_design_scores_gemma":[0.01456442,0.005146703,0.4631038,0.002344819,0.0003633655,0.0002258592,0.003753425,0.3109123,0.08031142,0.01971788,0.0943258,0.005230271],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4316857,0.00006297104,0.5641721,0.001225263,0.000603273,0.001498393,0.0001343405,0.0002496871,0.0003682621],"genre_scores_gemma":[0.8266445,0.00002940703,0.1708212,0.001452039,0.0005598734,0.0003086366,0.00009626756,0.00002192143,0.00006613894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9792491,"threshold_uncertainty_score":0.998343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.25754440802378,"score_gpt":0.4747668898643175,"score_spread":0.2172224818405375,"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."}}