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

By Philip Schmidt, Attila Reiss, Robert Dürichen, Claus Marberger, Kristof Van Laerhoven. International Conference on Multimodal Interaction, 2018

1,275 citations recorded by the source corpus.

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.

Subfields: Cardiology and Cardiovascular Medicine, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/f7d4957127bb35b0d3cb1042a676ea60e259463d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Pritam Sarkar
2. Ali Etemad
3. Sanchita Paul
4. Shruti Gedam

In Experimental and Cognitive Psychology

1. Pritam Sarkar
2. Ali Etemad
3. Sanchita Paul
4. Shruti Gedam

Citations in the corpus

• Self-Supervised ECG Representation Learning for Emotion Recognition (2020, methodology): “In the case of AMIGOS, DREAMER, and WESAD datasets, the performance improves significantly while marginal improvement is noticed for SWELL dataset.”
• A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques (2021, methodology): “Apublicly available dataset ‘‘WESAD’’ [70] was used by P. Bobade and M. Vani [71], where data of sensor modalities like ACC, ECG, BVP, BT, respiration, EMG, and EDA was used.”
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “WESAD [100] 2018 / / ■ / ■ ■ EDA, blood volume pulse & temperature, AAC 15 Neutral, Stress, Amusement”
• Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis (2023, methodology): “In the simulation experiments described in Section 3.4 and the applications to different physiological prediction problems in Sections 4.1, 4.2, and 4.3 we used 100 samples from the distribution of ω α to estimate the risk for a given α .”