{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004778242,0.0008500909,0.0007123812,0.0009300511,0.0003601254,0.0005789779,0.001700803,0.0008169627,0.005183749],"category_scores_gemma":[0.0008334511,0.0004276734,0.000951202,0.001021823,0.0004325154,0.0008560711,0.0009435428,0.001169101,0.002237184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001176247,"about_ca_system_score_gemma":0.00121397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02379438,"about_ca_topic_score_gemma":0.03644432,"domain_scores_codex":[0.9994747,0.0000637554,0.00002489536,0.0002416542,0.0001127389,0.00008229038],"domain_scores_gemma":[0.9997587,0.00004942993,0.00002327044,0.0000614739,0.00007958677,0.00002764253],"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":[0.0001579413,0.0001953052,0.00141066,0.0001608235,0.0001096518,0.00008375895,0.00007370149,0.1113254,0.01565961,0.008723881,0.01272513,0.8493741],"study_design_scores_gemma":[0.0000089582,0.00007115318,0.001160687,0.00001824324,0.00002273648,0.00005234108,0.00001868234,0.982092,0.00395334,0.008834432,0.003752602,0.00001474322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008835822,0.0008741486,0.9834267,0.0001925301,0.0001012933,0.0001089173,0.0006418295,0.002610998,0.003207707],"genre_scores_gemma":[0.4251221,0.001082277,0.5516167,0.0005548018,0.0001957806,0.0003193815,0.003573774,0.0002516389,0.01728352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02379438,"threshold_uncertainty_score":0.04731178,"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."}}