{"id":"W4206084855","doi":"10.1109/lsens.2021.3133887","title":"FFT Spectrum Spread With Machine Learning (ML) Analysis of Triaxial Acceleration From Shirt Pocket and Torso for Sensing Coughs While Walking","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; CMC Microsystems","keywords":"Torso; Accelerometer; Acceleration; Computer science; Artificial intelligence; Simulation; Medicine; Physics","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":[0.0002357046,0.0004164079,0.0002614175,0.0008673518,0.000111375,0.0002498518,0.0001700992,0.0003036744,0.0009612408],"category_scores_gemma":[0.001036973,0.00006808597,0.0003095401,0.0006954909,0.0001416236,0.000342229,0.0002229199,0.0002121514,0.0004118208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008286948,"about_ca_system_score_gemma":0.0001212629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007960766,"about_ca_topic_score_gemma":0.00114699,"domain_scores_codex":[0.9998653,0.00002545187,0.00001039336,0.00003133274,0.00005410679,0.00001343593],"domain_scores_gemma":[0.9997459,0.0001107498,0.00004421401,0.00002505204,0.00006058452,0.00001358789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006903285,0.0002528483,0.02342484,0.0002676743,0.0001048883,0.000546869,0.0002622653,0.04963493,0.1958396,0.001243608,0.002394844,0.7253373],"study_design_scores_gemma":[0.00001654978,0.0003889993,0.06127622,0.00003042508,0.00005201016,0.0006534796,0.0001612956,0.8891873,0.04473666,0.001260942,0.002197123,0.00003897261],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4840844,0.0007666618,0.5102021,0.0002383417,0.000117634,0.00006364001,0.0004302131,0.001157608,0.002939557],"genre_scores_gemma":[0.9241348,0.000314368,0.07360905,0.00005629798,0.00005647349,0.00003803275,0.0003254522,0.00005196061,0.001413561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009612408,"threshold_uncertainty_score":0.003215671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130197617709096,"score_gpt":0.2079657513201434,"score_spread":0.1949459895492338,"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."}}