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

WESAD records 15 lab participants wearing a chest RespiBAN (ECG, EDA, EMG, respiration, temperature, accelerometer) and a wrist Empatica E4 (BVP 64 Hz, EDA, temperature, accelerometer) through baseline, amusement (funny clips), stress (Trier Social Stress Test) and meditation. The paper sets the reference baselines: hand-crafted features with five classic classifiers under leave-one-subject-out validation, for a 3-class baseline / stress / amusement task and a binary stress task. Using only wrist physiology (BVP, EDA, temperature) a Random Forest reaches 76.2% accuracy and 66.3% macro F1 on the 3-class task; wrist BVP alone reaches 70.2% accuracy with LDA.

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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• WESADWearable Stress and Affect Detection15 subjects · PPG · ECG · IMU