WESAD (Wearable Stress and Affect Detection)
Linked on 9/13/2018
WESAD (Wearable Stress and Affect Detection) contains data of 15 subjects during a stress-affect lab study, while wearing physiological and motion sensors.
Dataset Characteristics
Multivariate, Time-Series
Subject Area
Computer Science
Associated Tasks
Classification, Regression
Feature Type
Real
# Instances
63000000
# Features
-
Dataset Information
Additional Information
WESAD is a publicly available dataset for wearable stress and affect detection. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects during a lab study. The following sensor modalities are included: blood volume pulse, electrocardiogram, electrodermal activity, electromyogram, respiration, body temperature, and three-axis acceleration. Moreover, the dataset bridges the gap between previous lab studies on stress and emotions, by containing three different affective states (neutral, stress, amusement). In addition, self-reports of the subjects, which were obtained using several established questionnaires, are contained in the dataset. Details can be found in the dataset's readme-file, as well as in [1].
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no |
0 to 1 of 1
Additional Variable Information
Raw sensor data was recorded with two devices: a chest-worn device (RespiBAN) and a wrist-worn device (Empatica E4). The RespiBAN device provides the following sensor data: electrocardiogram (ECG), electrodermal activity (EDA), electromyogram (EMG), respiration, body temperature, and three-axis acceleration. All signals are sampled at 700 Hz. The Empatica E4 device provides the following sensor data: blood volume pulse (BVP, 64 Hz), electrodermal activity (EDA, 4 Hz), body temperature (4 Hz), and three-axis acceleration (32 Hz). The dataset's readme-file contains all further details with respect to the dataset structure, data format (RespiBAN device, Empatica E4 device, synchronised data), study protocol, and the self-report questionnaires.
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset wesad_wearable_stress_and_affect_detection = fetch_ucirepo(id=465) # data (as pandas dataframes) X = wesad_wearable_stress_and_affect_detection.data.features y = wesad_wearable_stress_and_affect_detection.data.targets # metadata print(wesad_wearable_stress_and_affect_detection.metadata) # variable information print(wesad_wearable_stress_and_affect_detection.variables)
Schmidt, P. & Reiss, A. (2018). WESAD (Wearable Stress and Affect Detection) [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C57K5T.
Citations/Acknowledgements
If you use this dataset, please follow the acknowledgment policy on the original dataset website.
Creators
Philip Schmidt
Attila Reiss