{"id":"W4416960291","doi":"10.1109/embc58623.2025.11254344","title":"Using mmWave Radar and Deep Learning to Classify Caregiver Activities for Infection Prevention","year":2025,"lang":"en","type":"article","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute","funders":"Mitacs","keywords":"Deep learning; Transmission (telecommunications); Infection control; Radar; Focus (optics); Health care; Focus group","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006605434,0.00009845842,0.000101227,0.00014969,0.0001065902,0.00005282658,0.00002299781,0.00005984811,0.000005538704],"category_scores_gemma":[0.00003528515,0.0001094355,0.00003582863,0.000138458,0.000009971343,0.0002160253,0.00002997243,0.00008930588,0.000001297263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001696738,"about_ca_system_score_gemma":0.000009477947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005605919,"about_ca_topic_score_gemma":0.00007635602,"domain_scores_codex":[0.9995614,0.00001506665,0.00009135906,0.0001276758,0.00005332112,0.000151213],"domain_scores_gemma":[0.9997959,0.00007551835,0.00001278192,0.00005793748,0.00002470773,0.00003322132],"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":[0.00002506208,0.00001269083,0.0157994,0.0004374266,0.0001262209,0.000001230426,0.001007505,0.03126877,0.8516225,0.0009316314,0.0001246453,0.09864297],"study_design_scores_gemma":[0.001648022,0.0003130863,0.01857266,0.0007279749,0.0001907539,0.00001037271,0.003834573,0.06013289,0.8974517,0.00290172,0.01326792,0.000948346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5319337,0.00008477744,0.4652001,0.000007209954,0.0003539679,0.0001951474,4.602001e-7,0.0001492843,0.002075399],"genre_scores_gemma":[0.9897848,0.00002268333,0.009414329,0.000007112032,0.0000860455,0.0000292511,0.000001653204,0.00001925342,0.0006348228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4578512,"threshold_uncertainty_score":0.446265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133922547485662,"score_gpt":0.2778896608937407,"score_spread":0.2565504354188841,"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."}}