{"id":"W2955941909","doi":"10.1117/1.jbo.23.11.115001","title":"Stress assessment by means of heart rate derived from functional near-infrared spectroscopy","year":2018,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Norwegian Biodiversity Information Centre; Chongqing Science and Technology Commission; National Brain Mapping Laboratory; Cognitive Sciences and Technologies Council","keywords":"Functional near-infrared spectroscopy; Stress (linguistics); Computer science; Mental stress; Principal component analysis; Support vector machine; Heart rate; Pattern recognition (psychology); Artificial intelligence; Medicine; Psychology; Internal medicine; Cognition; Neuroscience; Blood pressure","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005582916,0.000747646,0.00051679,0.0008519016,0.0001220771,0.0004376693,0.0002150292,0.0004787139,0.0007168852],"category_scores_gemma":[0.001190785,0.0001205503,0.0002148105,0.0004563761,0.0002058174,0.0002993517,0.0002271629,0.0002839478,0.0002863472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007303965,"about_ca_system_score_gemma":0.0001135748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00057372,"about_ca_topic_score_gemma":0.0008331386,"domain_scores_codex":[0.9996351,0.0001019663,0.00002984087,0.0001076078,0.0001000597,0.00002540513],"domain_scores_gemma":[0.999684,0.00008523506,0.00008818897,0.00002131354,0.00009462009,0.00002667923],"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":[0.002589488,0.0002679598,0.09482778,0.0007627518,0.0002954588,0.0005942703,0.0009483759,0.002511511,0.5991732,0.0002509261,0.0008116657,0.2969665],"study_design_scores_gemma":[0.00008641076,0.003162375,0.8595175,0.00008438,0.0003291315,0.002364665,0.0005958636,0.0306427,0.100552,0.00075394,0.001745703,0.0001654679],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9342884,0.002524067,0.05987472,0.00008741141,0.0001073464,0.000151074,0.0005814526,0.0003027761,0.002082708],"genre_scores_gemma":[0.980408,0.001142284,0.01732991,0.00006109125,0.0000999228,0.0001031069,0.000323026,0.00002086927,0.0005117892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008519016,"threshold_uncertainty_score":0.002952516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134202838645141,"score_gpt":0.2528843018146319,"score_spread":0.2415422734281805,"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."}}