Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net

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 u003c 0.0001), laboratory verification: r = 0.70 (p u003c 0.0001), field test r = 0.56 (p u003c 0.0001)) with fine granularity rat...