Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection

Attila Reiss · Robert Dürichen · Claus Marberger · Kristof Van Laerhoven

1,356 citations0/0 files storedACM International Conference on Multimodal Interaction (ICMI)

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Emotion and Mood Recognition Heart Rate Variability and Autonomic Control Context-Aware Activity Recognition Systems

Abstract

Affect recognition aims to detect a person's affective state based on observables, with the goal to e.g. improve human-computer interaction. Long-term stress is known to have severe implications on wellbeing, which call for continuous and automated stress monitoring systems. However, the affective computing community lacks commonly used standard datasets for wearable stress detection which a) provide multimodal high-quality data, and b) include multiple affective states. Therefore, we introduce WESAD, a new 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. Furthermore, a benchmark is created on the dataset, using well-known features and standard machine learning methods. Considering the three-class classification problem ( baseline vs. stress vs. amusement ), we achieved classification accuracies of up to 80%,. In the binary case ( stress vs. non-stress ), accuracies of up to 93%, were reached. Finally, we provide a detailed analysis and comparison of the two device locations ( chest vs. wrist ) as well as the different sensor modalities.

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References

1988Development and validation of brief measures of positive and negative affect: The PANAS scales.1988Development and validation of brief measures of positive and negative affect: The PANAS scales.2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2001Toward machine emotional intelligence: analysis of affective physiological state 2005Detecting Stress During Real-World Driving Tasks Using Physiological Sensors

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2022A systematic review on affective computing: emotion models, databases, and recent advances 2021A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques 2023Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations 2020Self-Supervised ECG Representation Learning for Emotion Recognition 2019A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals 2020Stress Detection with Machine Learning and Deep Learning using Multimodal Physiological Data 2019A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition

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