 Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic netFenghua Li, Peida Xu, Shichun Zheng, Wenfeng Chen, Yang Yan, Suo Lu and Zhengkui Liu International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 9, 1550147718803298 Abstract: 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 = 0.72 (p  Keywords: Heart rate variability; stress detection; regression; field test; photoplethysmography (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:14:y:2018:i:9:p:1550147718803298 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
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