{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008087939,0.000241373,0.0008235556,0.0002683585,0.0006625065,0.0002061216,0.0009775378,0.0001407565,0.000007378478],"category_scores_gemma":[0.00007758039,0.0002312577,0.0002012544,0.0001147365,0.00009485569,0.001498028,0.0004265041,0.0006518508,0.000003298689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002469975,"about_ca_system_score_gemma":0.0001358049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001402121,"about_ca_topic_score_gemma":0.00001497586,"domain_scores_codex":[0.9979905,0.0002130408,0.0007718076,0.0003195686,0.0003295949,0.0003755056],"domain_scores_gemma":[0.9959143,0.0005439722,0.002628544,0.000579618,0.0002338861,0.00009970592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001681022,0.000095185,0.0003785337,0.0003870665,0.0002069355,0.00002730117,0.002070848,0.06044863,0.9291126,0.001534818,0.000001181605,0.005568834],"study_design_scores_gemma":[0.0009106536,0.0000927572,0.003120129,0.0009182293,0.00004701291,0.0005481399,0.003028572,0.9681013,0.022549,0.0003640178,0.000006727274,0.0003135211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9241749,0.00008056963,0.07379005,0.00002085079,0.0004435628,0.0001345784,0.000002714887,0.00004905105,0.00130372],"genre_scores_gemma":[0.9930666,0.00002161256,0.006615493,0.00000770827,0.0002182794,0.00000353528,5.180902e-7,0.00002823856,0.000037986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9076526,"threshold_uncertainty_score":0.9430417,"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."}}