{"id":"W4285044286","doi":"10.22215/etd/2022-15000","title":"Heart Rate Detection using Video Magnification: Impact of Algorithmic Parameters and Noise","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; AGE-WELL","keywords":"Quantization (signal processing); Magnification; Computer vision; Computer science; Artificial intelligence; Window (computing); Noise (video); Region of interest; Software","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001362504,0.0002535575,0.0002827077,0.0002818245,0.00007705724,0.00004242067,0.00008016717,0.0001316355,0.0001721733],"category_scores_gemma":[0.00003124154,0.0002749871,0.0001361682,0.0002878995,0.00001476864,0.0001766186,0.00001429012,0.0002616072,0.000006285431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003267735,"about_ca_system_score_gemma":0.00004085343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006777112,"about_ca_topic_score_gemma":0.00003798891,"domain_scores_codex":[0.9990598,0.00004140316,0.0003058408,0.0002374988,0.0001647435,0.0001906968],"domain_scores_gemma":[0.9994954,0.00008234808,0.00009137701,0.0002008061,0.00006058529,0.00006949275],"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":[0.00002741865,0.0000087034,0.0007687705,0.000304364,0.00009397299,0.000002331437,0.0001849181,0.01979802,0.9751667,0.000001627541,0.0000193572,0.003623818],"study_design_scores_gemma":[0.0004302483,0.0002212842,0.05270513,0.0001499002,0.0001798372,0.00002842457,0.0009287546,0.02566891,0.9187204,0.0001544172,0.00006895445,0.0007436947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958532,0.0003334062,0.001963703,9.206173e-7,0.0009488933,0.0003354416,0.0000208089,0.0001393323,0.0004042492],"genre_scores_gemma":[0.9969006,0.00009225698,0.002531687,0.000001662078,0.00008721703,0.00005167409,0.0001062988,0.00008160005,0.0001470216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05644625,"threshold_uncertainty_score":0.9999703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01467568820895779,"score_gpt":0.2708938486175489,"score_spread":0.2562181604085911,"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."}}