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Stress Recognition in Automobile Drivers

Stress Recognition in Automobile Drivers


Autonomous Driving

Data Type:

3D Model
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PhysioNet, the moniker of the Research Resource for Complex Physiologic Signals, was established in 1999 under the auspices of the National Institutes of Health (NIH), as described further below.

Data Preview ? 108.7M

    Data Structure ?


    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.


    When using this resource, please cite the original publication:

    Healey JA, Picard RW. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions in Intelligent Transportation Systems 6(2):156-166 (June 2005).

    Please include the standard citation for PhysioNet: (show more options)
    Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.