Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact

Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

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:




Attribute Characteristics:


Number of Attributes:


Date Donated


Associated Tasks:

Classification, Regression

Missing Values?


Number of Web Hits:



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

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'.

Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML