Stress Recognition in Automobile Drivers

@article{pollard_physionet_2026, title = {{PhysioNet} as a global platform for biomedical research}, volume = {1}, issn = {3005-0693}, url = {https://doi.org/10.1038/s44360-026-00096-z}, doi = {10.1038/s44360-026-00096-z}, abstract = {Twenty-five years after its first public repository was established, the PhysioNet resource for biomedical and clinical data has enabled tens of thousands of research papers. Further efforts are ongoing to advance secure data sharing, community engagement and scalable research infrastructure.}, number = {8}, journal = {Nature Health}, author = {Pollard, Tom and Moody, Benjamin E. and Lehman, Li-wei H. and Gow, Brian J. and Fernandes, Chrystinne and Xie, Chen and Johnson, Alistair and Mark, Roger G. and Heldt, Thomas}, month = aug, year = {2026}, pages = {792--795},}

Abstract

This database, contributed to PhysioNet by its creator, Jennifer Healey, contains a collection of multiparameter recordings from healthy volunteers, taken while they were driving on a prescribed route including city streets and highways in and around Boston, Massachusetts. The objective of the study for which these data were collected was to investigate the feasibility of automated recognition of stress on the basis of the recorded signals, which include ECG, EMG (right trapezius), GSR (galvanic skin resistance) measured on the hand and foot, and respiration.

Data Description

Records drive17a and drive17b are two parts of one experiment, lasting 29 and 25 minutes respectively; the other 16 records each contain a complete experiment, with durations of 65 to 93 minutes. For background information, details of the recordings, and discussion of the study and its conclusions, see the references above and below.

The stress ratings from the study are not available.

References

• Healey JA. Wearable and automotive systems for affect recognition from physiology. MIT Dept. of Electrical Engineering and Computer Science, Ph.D. thesis, 2000.

Cited by

6 publications cite this project. The most recent are listed below.

• Predicting stress in automobile drivers using LSTMs with feature engineering
  Mehmet Yasin Ulukuş, Enes Ahmeti, Hatice Tekiner Mogulkoc. Soft Computing, 2025.
• Design of Driver Stress Prediction Model with CNN-LSTM: Exploration of Feature Space using Genetic Programming
  Tingting Yang, Chenhao Xue, Jun Chen. International Joint Conference on Neural Networks (IJCNN), 2024.
• Experimental Study on Emergency Psychophysiological and Behavioral Reactions to Coal Mining Accidents
  Xiangchun Li, Yuzhen Long, Shuhao Zhang et al. Applied Psychophysiology and Biofeedback, 2024.
• Real-World Driver Stress Recognition and Diagnosis Based on Multimodal Deep Learning and Fuzzy EDAS Approaches
  Muhammad Amin, Khalil Ullah, Muhammad Asif et al. Diagnostics, 2023.
• Driver stress levels detection system using hyperparameter optimization
  Mohammad Naim Rastgoo, Bahareh Nakisa, Andry Rakotonirainy et al. Journal of Intelligent Transportation Systems, 2022.

View all 6 publications

Citation data from OpenAlex. Papers that use the project without citing it may be missing.