{"id":"W3120370179","doi":"10.22215/etd/2016-11682","title":"Heart Rate and Heart Rate Variability Estimation in the Presence of Motion Artifacts","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Heart rate variability; Heart rate; Computer science; Modalities; Gold standard (test); Computer vision; Pulse (music); Biomedical engineering; Artificial intelligence; Real-time computing; Medicine; Internal medicine; Telecommunications; Blood pressure","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":[0.0009856909,0.0001834418,0.0002286396,0.0001007271,0.00003478997,0.00003892668,0.00009666526,0.0001496668,0.00002782823],"category_scores_gemma":[0.0004480156,0.0001311269,0.00003746896,0.0001626184,0.00002397026,0.0002838468,0.00001069873,0.0001793988,0.00001838753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005339292,"about_ca_system_score_gemma":0.00002215636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009562247,"about_ca_topic_score_gemma":0.00006600766,"domain_scores_codex":[0.9989368,0.0001956361,0.0003218341,0.0002140053,0.0001559943,0.0001756721],"domain_scores_gemma":[0.9989756,0.0006048158,0.00005576261,0.0002598445,0.00006919369,0.00003482816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003121174,0.00003220025,0.006997197,0.0008007262,0.00001996745,0.000001387093,0.0008231286,0.004813688,0.9827545,0.0002912556,0.0001338006,0.003300965],"study_design_scores_gemma":[0.0002519711,0.00004069873,0.5369626,0.0004911577,0.0000223228,0.000002354851,0.0001858223,0.006072732,0.4501521,0.005495468,0.00004114744,0.0002816379],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889298,0.00005558361,0.007031429,0.00006048844,0.0005607593,0.0005482935,0.000007240788,0.00007539346,0.002730994],"genre_scores_gemma":[0.998912,0.00002136541,0.000760571,0.000007948022,0.00005108025,0.00005270574,0.00004166397,0.00002338896,0.0001292486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5326024,"threshold_uncertainty_score":0.5347201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01246543767686511,"score_gpt":0.252952098294432,"score_spread":0.2404866606175669,"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."}}