{"id":"W2942118652","doi":"10.1109/tbme.2019.2912407","title":"An Ultrasound-Based Biomedical System for Continuous Cardiopulmonary Monitoring: A Single Sensor for Multiple Information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Spirometer; Photoplethysmogram; Wearable computer; Analog front-end; SIGNAL (programming language); Transducer; Computer science; Biomedical engineering; Sensitivity (control systems); Ultrasonic sensor; Accelerometer; Continuous monitoring; Ultrasound; Respiratory monitoring; Real-time computing; Medicine; Acoustics; Computer vision; Respiratory system; Electronic engineering; Asthma; Engineering; Embedded system; Exhaled nitric oxide; Internal medicine; Radiology","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":[0.000743699,0.0006006646,0.000749209,0.0007321244,0.0003482817,0.0007314068,0.001235403,0.001534727,0.003981222],"category_scores_gemma":[0.00122259,0.0003668117,0.0003369575,0.0004592124,0.0004012586,0.001010962,0.001121129,0.0006427955,0.001801498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002454958,"about_ca_system_score_gemma":0.0005055679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002544886,"about_ca_topic_score_gemma":0.0004342431,"domain_scores_codex":[0.99905,0.0001723851,0.00005824944,0.0003016383,0.0003744413,0.00004330312],"domain_scores_gemma":[0.9994693,0.0001687657,0.00007181777,0.0000722866,0.0001598785,0.00005785559],"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":[0.0004165531,0.0001384102,0.001560262,0.0005637748,0.00007017498,0.000240048,0.0001620135,0.0005717924,0.7854581,0.001328441,0.002885892,0.2066045],"study_design_scores_gemma":[0.0003785846,0.006354957,0.0214029,0.0003569244,0.0006325647,0.01012238,0.0001873971,0.105086,0.7733715,0.002553903,0.07920188,0.0003509988],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1165036,0.005959498,0.8631327,0.001109903,0.001280316,0.0006552238,0.0004179961,0.004081087,0.00685973],"genre_scores_gemma":[0.4574578,0.001907107,0.524011,0.001468358,0.0006526441,0.0006525212,0.0002901051,0.000108788,0.01345177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003981222,"threshold_uncertainty_score":0.01331854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008735994472557079,"score_gpt":0.2063418941932871,"score_spread":0.1976058997207301,"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."}}