{"id":"W4311413841","doi":"10.1109/sensors52175.2022.9967327","title":"Non-Visual and Contactless Wellness Monitoring for Long Term Care Facilities Using mm-Wave Radar Sensors","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Sensors","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Radar; Computer science; Supervisor; Real-time computing; Deep learning; Assisted living; Term (time); Artificial intelligence; Remote patient monitoring; Simulation; Computer vision; Medicine; Telecommunications; Nursing","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":[0.0001710952,0.0004063826,0.0003491607,0.0002728202,0.0001447547,0.0003545615,0.0005755912,0.0005177092,0.0007688279],"category_scores_gemma":[0.0003500232,0.0001499713,0.0002816186,0.0002271438,0.0001001104,0.0005167607,0.0003729858,0.0002931528,0.0004408644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001662953,"about_ca_system_score_gemma":0.000192595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005205599,"about_ca_topic_score_gemma":0.001159967,"domain_scores_codex":[0.999764,0.00003980975,0.00001423106,0.00006153976,0.00009805323,0.00002240706],"domain_scores_gemma":[0.9998415,0.00002585879,0.0000375638,0.00001893059,0.00006156543,0.00001453575],"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":[0.0004250232,0.0004287862,0.02187812,0.0003551657,0.0001188432,0.0005992402,0.0001773241,0.01747848,0.4503188,0.001212481,0.004443346,0.5025645],"study_design_scores_gemma":[0.00008695536,0.001902137,0.04379408,0.0000868796,0.0002011376,0.002401614,0.000189359,0.7209377,0.2179722,0.001097168,0.0112193,0.0001114678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2594698,0.001833973,0.731306,0.0006130736,0.0002682529,0.0001174817,0.0002836192,0.002004294,0.004103445],"genre_scores_gemma":[0.8615654,0.0005306671,0.1345693,0.000475902,0.00008574872,0.00009366403,0.0002042371,0.00002181829,0.002453205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007688279,"threshold_uncertainty_score":0.002572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193582052834003,"score_gpt":0.2509893904228697,"score_spread":0.2290535698945297,"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."}}