{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003257118,0.0007660427,0.0005633751,0.000375147,0.0001269565,0.0003582647,0.0005305485,0.000515014,0.00312749],"category_scores_gemma":[0.0006737467,0.0002345881,0.0004167741,0.0003362717,0.0001765629,0.0004900841,0.0006900606,0.0004756348,0.001682711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001010364,"about_ca_system_score_gemma":0.0002435431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009271994,"about_ca_topic_score_gemma":0.001954929,"domain_scores_codex":[0.9996998,0.00005702454,0.000009037352,0.00007763463,0.0001157816,0.00004065113],"domain_scores_gemma":[0.9998586,0.00003391119,0.00002471554,0.0000284524,0.00003734773,0.00001698313],"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":[0.0005749175,0.0001557808,0.004583045,0.0002611676,0.0001214015,0.0002365281,0.0001253958,0.0334533,0.1804348,0.001773517,0.005170093,0.77311],"study_design_scores_gemma":[0.00005960953,0.0006645389,0.03840521,0.00006683647,0.0001049612,0.001878612,0.0001544298,0.8415294,0.09749408,0.005052463,0.01450269,0.00008726618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03263654,0.0005093703,0.9632374,0.0000706748,0.00008207298,0.0000554481,0.0003084941,0.001364231,0.001735797],"genre_scores_gemma":[0.4946148,0.001160173,0.4909987,0.0003750751,0.00024457,0.0001514917,0.001808904,0.0002350782,0.01041121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00312749,"threshold_uncertainty_score":0.01046246,"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."}}