{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001013476,0.001135888,0.0008581308,0.001805356,0.0002577257,0.0005783172,0.000753686,0.0006579323,0.001124976],"category_scores_gemma":[0.002842238,0.0002975003,0.001164581,0.001670047,0.0004628967,0.0006026811,0.0006969204,0.0008167085,0.0006678865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004114415,"about_ca_system_score_gemma":0.000583949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006563317,"about_ca_topic_score_gemma":0.009132487,"domain_scores_codex":[0.9992796,0.0001574862,0.00006031402,0.0003018273,0.0001272131,0.00007368209],"domain_scores_gemma":[0.999171,0.000323014,0.0001603237,0.0001350687,0.0001618036,0.00004881901],"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":[0.000483966,0.0002541685,0.02254764,0.0004945208,0.0003806435,0.0003351944,0.0003992398,0.215845,0.0363069,0.002944999,0.007437718,0.71257],"study_design_scores_gemma":[0.00002153463,0.0001277292,0.02646081,0.00004660935,0.00004809155,0.0001569532,0.0001403439,0.9548309,0.00756775,0.006395705,0.004165715,0.0000378672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.072812,0.001214194,0.9202058,0.000304486,0.0001486312,0.0001349724,0.001240923,0.003012064,0.000926914],"genre_scores_gemma":[0.6333252,0.001022474,0.3586112,0.0002241037,0.0002912831,0.0003169015,0.004046992,0.0002956014,0.001866275],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006563317,"threshold_uncertainty_score":0.0130502,"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."}}