{"id":"W2622156485","doi":"10.1152/japplphysiol.00299.2017","title":"Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models","year":2017,"lang":"en","type":"article","venue":"Journal of Applied Physiology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministério da Ciência, Tecnologia e Inovação; Canada Research Chairs; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; AGE-WELL","keywords":"Wearable computer; Dynamics (music); Activities of daily living; Aerobic exercise; Computer science; Physical activity; Artificial intelligence; Machine learning; Physical medicine and rehabilitation; Psychology; Medicine; Physical therapy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003852465,0.0005006546,0.0003758327,0.0003079418,0.0001263514,0.0003251964,0.0002387859,0.0002614337,0.0005351084],"category_scores_gemma":[0.001181575,0.0001686376,0.0004076477,0.0003014162,0.000125937,0.0003154835,0.0002180925,0.0003344711,0.0002598466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001901102,"about_ca_system_score_gemma":0.0003236222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00442912,"about_ca_topic_score_gemma":0.006358657,"domain_scores_codex":[0.9998851,0.00002532915,0.000007355451,0.00004657317,0.00001817477,0.00001745009],"domain_scores_gemma":[0.9996701,0.0001870017,0.00006196942,0.00002738602,0.00004075608,0.0000126973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003242856,0.0004139229,0.07256116,0.0001564975,0.000270762,0.0002011914,0.0002423685,0.6528379,0.02467308,0.0007225287,0.001063544,0.2465326],"study_design_scores_gemma":[0.000004419318,0.00004733554,0.0212588,0.000007037163,0.00001247388,0.00002770865,0.00001915341,0.9768332,0.001117352,0.0004965955,0.0001688602,0.000007076755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5349817,0.0002688216,0.4625978,0.00009005547,0.0000209352,0.00007456824,0.0003905824,0.0006103051,0.0009652125],"genre_scores_gemma":[0.9688149,0.0001052743,0.02973738,0.0000201259,0.00001367684,0.00007503975,0.0004326637,0.00002045967,0.0007804533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00442912,"threshold_uncertainty_score":0.008806705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03739397527611425,"score_gpt":0.2484498809612511,"score_spread":0.2110559056851369,"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."}}