{"id":"W3178681001","doi":"10.3390/s21144713","title":"Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal; Institut National de la Recherche Scientifique; Université TÉLUQ","funders":"Canada Research Chairs","keywords":"Activity recognition; Wearable computer; Computer science; Artificial intelligence; Machine learning; Feature engineering; Accelerometer; Classifier (UML); Wearable technology; Feature (linguistics); Deep learning; Embedded system","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":[0.000347881,0.0001635047,0.0003088531,0.0001086824,0.0001767654,0.00009146644,0.0001110718,0.00008245158,0.000003485213],"category_scores_gemma":[0.0003037231,0.0001698008,0.000110702,0.0002553519,0.00004498707,0.0002869713,0.00005217482,0.0003099575,0.000004310169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003588414,"about_ca_system_score_gemma":0.00004960624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003790314,"about_ca_topic_score_gemma":0.00002626988,"domain_scores_codex":[0.9982613,0.0005742059,0.0001441161,0.0005416014,0.0002461788,0.0002326451],"domain_scores_gemma":[0.9986719,0.0006408569,0.0001776486,0.0002226121,0.0001815461,0.0001054294],"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":[0.0002318245,0.0007884845,0.001244711,0.0001799993,0.00005709197,0.00001129989,0.00174185,0.01806974,0.03301674,0.0001682646,0.00000905587,0.944481],"study_design_scores_gemma":[0.0008289879,0.000389913,0.0006266254,0.00003295387,0.00001550531,0.00001823982,0.0003489363,0.9848098,0.01230821,0.0001421042,0.0002808269,0.0001979235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5674405,0.00001363473,0.4308524,0.00008692239,0.0000488341,0.0002016067,0.000005254997,0.00009792304,0.001253037],"genre_scores_gemma":[0.982358,0.00000524627,0.01727748,0.00003606032,0.00005765661,0.0000350072,0.00004053972,0.00001844654,0.0001715296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.96674,"threshold_uncertainty_score":0.6924275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0546264820847625,"score_gpt":0.2832454706733922,"score_spread":0.2286189885886297,"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."}}