{"id":"W3082120068","doi":"10.3390/s20174946","title":"A Hierarchical Learning Approach for Human Action Recognition","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Action recognition; Domain (mathematical analysis); Action (physics); Machine learning; Focus (optics); Inertial measurement unit; RGB color model; Activity recognition; Human–computer interaction","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":[0.0001007019,0.00008229675,0.00009225849,0.0000591668,0.0003251523,0.000137756,0.0001230391,0.00005764067,0.00002523487],"category_scores_gemma":[0.00005733241,0.00008466595,0.00007416856,0.0001600559,0.00002235881,0.0002224674,0.00002839859,0.0001997311,0.00009442897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001626165,"about_ca_system_score_gemma":0.00001371778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003774732,"about_ca_topic_score_gemma":7.954223e-7,"domain_scores_codex":[0.9992201,0.00007308921,0.0001348397,0.0002941611,0.0001205262,0.0001572741],"domain_scores_gemma":[0.9996859,0.00003858932,0.00006167144,0.00007975961,0.00005679858,0.00007728946],"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":[0.0001229463,0.0002921734,0.0001585859,0.0002767491,0.00009203029,0.0000128806,0.007028981,0.003613097,0.05415213,0.01345387,0.003776817,0.9170197],"study_design_scores_gemma":[0.001576627,0.0008117295,0.0005730868,0.00002562246,0.00003240515,0.00003426019,0.0004715809,0.8960714,0.03750041,0.01409882,0.04819972,0.0006043611],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4054462,0.000003702596,0.5882245,0.0009209529,0.00008767524,0.0002414046,0.00000275608,0.0004026514,0.004670142],"genre_scores_gemma":[0.9697115,0.000003391408,0.02873166,0.0007139216,0.0004020991,0.00003141934,0.00008946103,0.000011586,0.0003049984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9164154,"threshold_uncertainty_score":0.3452577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1011026685981778,"score_gpt":0.2954942585995387,"score_spread":0.1943915900013609,"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."}}