{"id":"W3187326345","doi":"10.1109/bhi50953.2021.9508570","title":"Body Pose Analysis using CNN and Pressure Sensor Array Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Computer science; Supine position; Recall; Artificial intelligence; Abnormality; Precision and recall; Artificial neural network; Pressure sensor; Data modeling; Machine learning; Pattern recognition (psychology); Data mining; Engineering; Medicine; Database","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":[0.0002137175,0.000778941,0.0003535788,0.0007006393,0.0001241377,0.0003808999,0.00032526,0.000355074,0.001898525],"category_scores_gemma":[0.0007184555,0.0001900807,0.0003882591,0.0005002674,0.0001525154,0.0003189241,0.0004128497,0.0002797369,0.000863919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003103062,"about_ca_system_score_gemma":0.0002402955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006437166,"about_ca_topic_score_gemma":0.008623677,"domain_scores_codex":[0.9997892,0.00002451103,0.000008205006,0.0000661727,0.00007331191,0.00003867719],"domain_scores_gemma":[0.9998789,0.00002654448,0.00002242026,0.000016632,0.00004550769,0.000009971157],"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":[0.0006511388,0.0002415001,0.03404998,0.0001419373,0.0002048553,0.0005864566,0.000110982,0.1013771,0.09384213,0.00062677,0.0042828,0.7638842],"study_design_scores_gemma":[0.00000899618,0.0002647678,0.05659201,0.00002638306,0.0000434576,0.0004172281,0.00008259805,0.9120618,0.02768103,0.0006795601,0.002118464,0.00002376368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5802106,0.0009722282,0.4042009,0.0002825688,0.0002548956,0.0002035946,0.00256499,0.003521277,0.007788893],"genre_scores_gemma":[0.9543402,0.0003928078,0.03889263,0.00009384356,0.00005072523,0.00006784467,0.001642907,0.00004972239,0.004469286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006437166,"threshold_uncertainty_score":0.01279938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03967919365457934,"score_gpt":0.3328657646786414,"score_spread":0.293186571024062,"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."}}