{"id":"W4240339891","doi":"10.22215/etd/2010-08789","title":"Posture recognition and postural transition detection using bed-based pressure sensor arrays","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario","keywords":"Pressure sensor; Physical medicine and rehabilitation; Computer science; Psychology; Artificial intelligence; Speech recognition; Engineering; Medicine; Mechanical engineering","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":[0.0003001616,0.0004032089,0.0004148509,0.0003501062,0.0002638874,0.0008189837,0.0003423416,0.0008282729,0.00388239],"category_scores_gemma":[0.0009699285,0.0002609882,0.0002946755,0.0002356316,0.0002201351,0.000546689,0.0003291656,0.0006000704,0.002037586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002129468,"about_ca_system_score_gemma":0.0003490803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00274531,"about_ca_topic_score_gemma":0.00280743,"domain_scores_codex":[0.9996018,0.00004170163,0.00001677766,0.0001121837,0.0001808019,0.00004680554],"domain_scores_gemma":[0.9994948,0.0001318992,0.00002332479,0.0000360668,0.0002775572,0.00003635855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005644789,0.0001885324,0.005553611,0.0001849331,0.00006101816,0.0005700638,0.0007553051,0.003375398,0.5675789,0.00206317,0.01089117,0.4082135],"study_design_scores_gemma":[0.00008222263,0.001616066,0.06565525,0.00007222927,0.0001093758,0.002581246,0.0007719377,0.1418423,0.7267072,0.001725896,0.05868403,0.0001522704],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3022329,0.002628302,0.6566425,0.00497306,0.00268322,0.0001867505,0.0006193859,0.00255654,0.0274773],"genre_scores_gemma":[0.7205676,0.001561414,0.1423667,0.0007686199,0.0009305145,0.0001290971,0.0004350753,0.0001458125,0.1330951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00388239,"threshold_uncertainty_score":0.01298785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01172458906115555,"score_gpt":0.2146675963423043,"score_spread":0.2029430072811487,"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."}}