{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007034704,0.0003488366,0.0003053196,0.0003427321,0.0001375172,0.0001133435,0.00004510405,0.0007150645,0.0003345076],"category_scores_gemma":[0.0000147093,0.0003520825,0.0001644742,0.0002020555,0.00001552595,0.0001820605,0.000001690827,0.0006470754,0.00002980814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002646759,"about_ca_system_score_gemma":0.00001732189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001111813,"about_ca_topic_score_gemma":0.001505119,"domain_scores_codex":[0.9989776,0.00003515499,0.0002824584,0.0003030658,0.0001969265,0.0002048072],"domain_scores_gemma":[0.9994776,0.00002491013,0.00007772788,0.0001233698,0.0001997766,0.00009660178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008495956,0.00003002189,0.0000119931,0.0007446744,0.0002443789,0.000005302784,0.0005257853,0.001802441,0.9690692,4.871815e-7,0.00002988696,0.02745084],"study_design_scores_gemma":[0.001609811,0.0001169816,0.004233744,0.0005571563,0.003237101,0.00007016503,0.00268933,0.4390929,0.5454423,0.0002930278,0.000826625,0.001830885],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994187,0.0002260069,0.002751372,0.00002388294,0.0006893747,0.0002907199,0.0001651677,0.0004573439,0.001209157],"genre_scores_gemma":[0.9919038,0.0000610054,0.001668749,0.00007185447,0.0002489193,0.00002126333,0.005663165,0.00008084873,0.0002803578],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4372904,"threshold_uncertainty_score":0.9998931,"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."}}