{"id":"W2247325373","doi":"10.1109/icsens.2015.7370271","title":"Optimizing pressure sensor array data for a smart-shoe fall monitoring system","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Falling (accident); Pressure sensor; Computer science; Foot pressure; Safety monitoring; Trajectory; Real-time computing; Engineering; Mechanical engineering; Medicine","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":[0.0002141935,0.000605649,0.0008796946,0.0006679489,0.0003430123,0.0005141493,0.0007318851,0.0004620219,0.002678554],"category_scores_gemma":[0.0008892374,0.000308398,0.0002798191,0.0006390215,0.0001292271,0.0005585412,0.0002799693,0.0002909267,0.001311365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000351816,"about_ca_system_score_gemma":0.0004924808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001881775,"about_ca_topic_score_gemma":0.002806536,"domain_scores_codex":[0.9996108,0.00004676755,0.00003483596,0.0001148966,0.00015613,0.0000366673],"domain_scores_gemma":[0.9995545,0.00008845431,0.00006046995,0.00005283985,0.0002166564,0.00002713477],"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":[0.001181776,0.0006523264,0.01731599,0.0003802601,0.0001134096,0.0005662453,0.0001652413,0.06732495,0.5115738,0.000664947,0.005213451,0.3948476],"study_design_scores_gemma":[0.0001366521,0.001235582,0.03234587,0.00002550959,0.0001038088,0.000422513,0.0001565307,0.7130136,0.2456946,0.0006498755,0.006151407,0.00006403382],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3841848,0.0004098551,0.6018634,0.0004550025,0.0001637339,0.0003990855,0.001240448,0.007864404,0.003419292],"genre_scores_gemma":[0.790288,0.0001383145,0.2059206,0.0001578775,0.00005391691,0.0003060943,0.0007875536,0.00007576835,0.002271926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002678554,"threshold_uncertainty_score":0.008960605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1589504030863628,"score_gpt":0.3129102946131226,"score_spread":0.1539598915267598,"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."}}