Version 1
: Received: 5 July 2024 / Approved: 5 July 2024 / Online: 9 July 2024 (10:11:18 CEST)
How to cite:
Moser, M.; Ehrhart, M.; Resch, B. An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements. Preprints2024, 2024070707. https://doi.org/10.20944/preprints202407.0707.v1
Moser, M.; Ehrhart, M.; Resch, B. An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements. Preprints 2024, 2024070707. https://doi.org/10.20944/preprints202407.0707.v1
Moser, M.; Ehrhart, M.; Resch, B. An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements. Preprints2024, 2024070707. https://doi.org/10.20944/preprints202407.0707.v1
APA Style
Moser, M., Ehrhart, M., & Resch, B. (2024). An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements. Preprints. https://doi.org/10.20944/preprints202407.0707.v1
Chicago/Turabian Style
Moser, M., Maximilian Ehrhart and Bernd Resch. 2024 "An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements" Preprints. https://doi.org/10.20944/preprints202407.0707.v1
Abstract
Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose an Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE) , to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress detection methodologies in terms of precision, recall and interpretability. Results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity, and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on.
Keywords
Stress Detection; Deep Learning; Explainable AI; LSTM; Deep Generative Ensemble; Generative Adversarial Network; Physiological Sensor Data; Wearable Sensors
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
