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WESAD (Wearable Stress and Affect Detection) Data Set
Download: Data Folder, Data Set Description

Abstract: WESAD (Wearable Stress and Affect Detection) contains data of 15 subjects during a stress-affect lab study, while wearing physiological and motion sensors.

Data Set Characteristics:  

Multivariate, Time-Series

Number of Instances:

63000000

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

12

Date Donated

2018-09-14

Associated Tasks:

Classification, Regression

Missing Values?

N/A

Number of Web Hits:

3043


Source:

Philip Schmidt, Robert Bosch GmbH, Corporate Research, Germany, firstname.lastname '@' de.bosch.com
Attila Reiss, Robert Bosch GmbH, Corporate Research, Germany, firstname.lastname '@' de.bosch.com


Data Set 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].


Attribute 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.


Relevant Papers:

[1] Philip Schmidt, Attila Reiss, Robert Duerichen, Claus Marberger and Kristof Van Laerhoven. 2018. Introducing WESAD, a multimodal dataset for Wearable Stress and Affect Detection. In 2018 International Conference on Multimodal Interaction (ICMI ’18), October 16–20, 2018, Boulder, CO, USA. ACM, New York, NY, USA, 9 pages. [Web Link]



Citation Request:

You may use this data for scientific, non-commercial purposes, provided that you give credit to the owners when publishing any work based on this data. Please acknowledge publication [1].
We recommend to refer to this dataset as WESAD, or 'Wearable Stress and Affect Detection'.


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