{"id":"W4399990340","doi":"10.1109/iotdi61053.2024.00020","title":"SUPER: Seated Upper Body Pose Estimation using mmWave Radars","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Torso; Computer science; Point cloud; Computer vision; Radar; Artificial intelligence; Pose; Fuse (electrical); Motion (physics); Task (project management); Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004840902,0.0003578694,0.0003949453,0.0003890752,0.00009091444,0.001015989,0.0007170875,0.0003909249,0.00007869293],"category_scores_gemma":[0.00004827433,0.0003048443,0.0002035126,0.0004156927,0.00002799718,0.0002441652,0.001740196,0.000635118,0.0009193824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001970079,"about_ca_system_score_gemma":0.0004064439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002773054,"about_ca_topic_score_gemma":0.00001013496,"domain_scores_codex":[0.9975508,0.0001694699,0.0005050221,0.0009156452,0.0005327276,0.0003263235],"domain_scores_gemma":[0.9985431,0.00006990244,0.0001175268,0.0008827109,0.0002382917,0.000148431],"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":[0.00007862286,0.00119743,0.002255657,0.01237834,0.003984354,0.002688078,0.03212916,0.06380431,0.03619077,0.1877131,0.07323878,0.5843413],"study_design_scores_gemma":[0.000169027,0.00002035848,0.0001121877,0.0008463284,0.00005399362,0.0001944691,0.00004418721,0.978165,0.004754332,0.01355878,0.001557581,0.0005237887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04918404,0.0007663311,0.9259316,0.001374984,0.006545186,0.0008383038,0.00002965038,0.001311272,0.01401861],"genre_scores_gemma":[0.8704087,0.00001751899,0.1267806,0.0002737456,0.0004214999,0.00003492416,0.00007168386,0.00004487376,0.001946474],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9143606,"threshold_uncertainty_score":0.9999404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03042228845481458,"score_gpt":0.2934427060186901,"score_spread":0.2630204175638755,"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."}}