{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002065071,0.0001810914,0.0002479713,0.00009052274,0.000287466,0.00009044139,0.0001501735,0.00005930301,0.000006722156],"category_scores_gemma":[0.00001014499,0.0001835918,0.00006386308,0.000154068,0.0000190126,0.0001419118,0.00007430224,0.000154133,0.000008094534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002677682,"about_ca_system_score_gemma":0.00002132147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004146626,"about_ca_topic_score_gemma":9.11411e-7,"domain_scores_codex":[0.9989851,0.00001757574,0.0002477189,0.00021849,0.0001872218,0.0003439029],"domain_scores_gemma":[0.9995326,0.0001194581,0.00004272447,0.00017482,0.00003046097,0.00009990345],"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":[0.00007567756,0.0001113128,0.0261761,0.001575421,0.000402711,0.00006677512,0.002255879,0.1274829,0.8248422,0.001205104,0.001458737,0.01434727],"study_design_scores_gemma":[0.01618951,0.007648387,0.006268052,0.002374902,0.0006672914,0.001601828,0.1007529,0.08372699,0.6281887,0.001053467,0.143081,0.008447062],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.968654,0.01221846,0.0077971,0.000004969144,0.008860328,0.001267079,0.00007063496,0.0004524296,0.0006749796],"genre_scores_gemma":[0.9980279,0.00006743838,0.00009073272,0.00000203939,0.0007538392,0.0007885863,0.00001310881,0.00005282199,0.0002035234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1966535,"threshold_uncertainty_score":0.7486658,"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."}}