{"id":"W2621464581","doi":"10.3390/s17061287","title":"Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Inertial measurement unit; Activity recognition; Classifier (UML); Wearable computer; Pattern recognition (psychology); Support vector machine; Machine learning; Data mining","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":[0.002204186,0.0008251608,0.001326205,0.001215277,0.0004606046,0.0009537621,0.00215474,0.001073084,0.001059187],"category_scores_gemma":[0.004198879,0.0005130089,0.001448784,0.001269088,0.0008360214,0.001124118,0.001134905,0.001153088,0.0005608415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008522299,"about_ca_system_score_gemma":0.001254639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006039848,"about_ca_topic_score_gemma":0.004597513,"domain_scores_codex":[0.9985167,0.0003733357,0.0001311627,0.0004203659,0.0004018804,0.0001565472],"domain_scores_gemma":[0.9985018,0.0006013853,0.0001656074,0.0001446819,0.000530752,0.00005588932],"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":[0.0001705299,0.0003002567,0.002520984,0.000114376,0.0001364995,0.00009213701,0.0002254339,0.5681829,0.01042139,0.006770066,0.0008020686,0.4102633],"study_design_scores_gemma":[0.000002748378,0.0000372785,0.0001907321,0.000003373028,0.000005578983,0.00001312442,0.000006824062,0.9972498,0.001335154,0.0009646341,0.0001857301,0.000005121877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007779822,0.00008100839,0.9914687,0.00003073923,0.000006734196,0.00005130037,0.00001650365,0.0003444379,0.0002207522],"genre_scores_gemma":[0.3469781,0.0001773655,0.6500565,0.00009091986,0.00004998911,0.0005485816,0.0003810416,0.00008659391,0.001630951],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006039848,"threshold_uncertainty_score":0.01200938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2074829021052562,"score_gpt":0.3808739446268515,"score_spread":0.1733910425215953,"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."}}