{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:17:49Z","timestamp":1776334669327,"version":"3.51.2"},"reference-count":60,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2018,9,1]],"date-time":"2018-09-01T00:00:00Z","timestamp":1535760000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Commission","doi-asserted-by":"crossref","award":["JCYJ20170413170301569"],"award-info":[{"award-number":["JCYJ20170413170301569"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Huawei Technologies Co., Ltd."}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2018,9]]},"abstract":"<jats:p>Detecting psychological stress in daily life is useful to stress management. However, existing stress-detection models with only heartbeat\/pulse input are limited in prediction output granularity, and models with multiple prediction levels output usually require additional bio-signal other than heartbeat, which may increase the number of sensors and be wearable unfriendly. In this study, we took a novel approach of incremental pulse rate variability and elastic-net regression in predicting mental stress. Mental arithmetic task paradigm was used during the experiments. A total of 178 participants involved in the model building, and the model was verified with a group of 29 participants in the laboratory and 40 participants in a 14-day follow-up field test. The result showed significant median correlations between self-report and model-prediction stress levels (cross-validation: r\u2009=\u20090.72 (p\u2009&lt;\u20090.0001), laboratory verification: r\u2009=\u20090.70 (p\u2009&lt;\u20090.0001), field test r\u2009=\u20090.56 (p\u2009&lt;\u20090.0001)) with fine granularity ratings of 0\u20137 float numbers. The correct prediction took 86%\u201391% of the testing samples with error standard deviation of 0.68\u20130.81 in the label space of 14. By simplifying the process of prediction with a perspective of stress difference and handling the collinearity among pulse rate variability features with elastic net, we successfully built a stress prediction model with only pulse rate variability input source, fine granularity output and portable friendly sensor.<\/jats:p>","DOI":"10.1177\/1550147718803298","type":"journal-article","created":{"date-parts":[[2018,9,28]],"date-time":"2018-09-28T01:17:21Z","timestamp":1538097441000},"page":"155014771880329","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":30,"title":["Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net"],"prefix":"10.1177","volume":"14","author":[{"given":"Fenghua","family":"Li","sequence":"first","affiliation":[{"name":"Key Lab of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, People\u2019s Republic of 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