{"id":"W4225261496","doi":"10.34133/2022/9780497","title":"Mobile Robotic Platform for Contactless Vital Sign Monitoring","year":2022,"lang":"en","type":"article","venue":"Cyborg and Bionic Systems","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute on Drug Abuse; Massachusetts Institute of Technology; National Institutes of Health; Brigham and Women's Hospital","keywords":"Vital signs; Computer science; Respiratory rate; Compensation (psychology); Real-time computing; Simulation; Heart rate; Wearable computer; Computer vision; Artificial intelligence; Embedded system; 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":[0.0002648669,0.000767695,0.0006036654,0.0006603835,0.0002227436,0.0004435767,0.001514532,0.0009266952,0.005119786],"category_scores_gemma":[0.0006605781,0.0003314236,0.000366917,0.0001810224,0.0002013139,0.0005466609,0.0009503392,0.0004616893,0.001998005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002051246,"about_ca_system_score_gemma":0.0004022344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006252904,"about_ca_topic_score_gemma":0.0005816582,"domain_scores_codex":[0.9994286,0.00004967623,0.00003078589,0.0001570923,0.0002705378,0.00006331247],"domain_scores_gemma":[0.9996995,0.00004417403,0.00006794108,0.00003982085,0.000103085,0.00004553195],"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":[0.0009021655,0.0004236443,0.003063733,0.0009070825,0.00008899659,0.001307888,0.00026698,0.005013474,0.6173994,0.002265874,0.0193877,0.3489731],"study_design_scores_gemma":[0.0006196208,0.008568098,0.0385067,0.0003520624,0.0003427432,0.01018253,0.0002709083,0.2876977,0.4263701,0.003038482,0.2233743,0.0006767995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1828763,0.003269629,0.7714493,0.0009053578,0.001490437,0.001261993,0.001312485,0.01838689,0.01904763],"genre_scores_gemma":[0.7680008,0.0008496239,0.2015959,0.0011037,0.0003146779,0.001215506,0.0009918314,0.0002354001,0.02569255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005119786,"threshold_uncertainty_score":0.01712734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0201989749629945,"score_gpt":0.2280344797131821,"score_spread":0.2078355047501876,"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."}}