Name
Title | ||
|---|---|---|
Photoplethysmography Based Psychological Stress Detection With Pulse Rate Variability Feature Differences And Elastic Net |
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<0.0001), laboratory verification: r=0.70 (p<0.0001), field test r=0.56 (p<0.0001)) with fine granularity ratings of 0-7 float numbers. The correct prediction took 86%-91% of the testing samples with error standard deviation of 0.68-0.81 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1177/1550147718803298 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Keywords | Field | DocType |
Heart rate variability, stress detection, regression, field test, photoplethysmography | Heartbeat,Regression,Simulation,Computer science,Elastic net regularization,Heart rate variability,Photoplethysmogram,Stress management,Pulse (signal processing),Granularity,Distributed computing | Journal |
Volume | Issue | ISSN |
14 | 9 | 1550-1477 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fenghua Li | 1 | 263 | 34.70 |
| Peida Xu | 2 | 0 | 0.34 |
| Shichun Zheng | 3 | 0 | 0.34 |
| Wenfeng Chen | 4 | 56 | 4.75 |
| Yang Yan | 5 | 0 | 0.34 |
| Suo Lu | 6 | 0 | 0.34 |
| Zhengkui Liu | 7 | 0 | 0.34 |