Department of Geoinformatics, University of Salzburg, 5020 Salzburg, Austria.
Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA.
Sensors (Basel). 2024 Aug 6;24(16):5085. doi: 10.3390/s24165085.
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 a 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. The 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.
压力对人类健康有多种影响。最近可穿戴传感器的发展取得了成功，结合深度学习技术自动从原始数据中检测特征，为检测情绪状态开辟了一些有趣的应用。能够在急性环境中准确检测与压力相关的情绪唤醒，可以积极影响人类的即时健康状况，例如，避免城市交通环境中的危险地点。这项工作提出了一种用于自动检测生理传感器数据中压力的可解释深度学习方法，该数据是通过非侵入式可穿戴传感器设备 Empatica E4 腕带记录的。我们提出了一个长短期记忆 (LSTM) 网络，通过深度生成式对抗网络 (DGE) 的条件生成器扩展，以应对传感器测量值稀疏标记的低数据情况。由于可解释性通常是深度学习模型的主要关注点之一，我们利用集成梯度 (IG) 来突出模型用于预测的最关键特征，并将结果与基于专家的最新压力检测方法在精确性、召回率和可解释性方面进行比较。结果表明，我们的 LSTM DGE 在召回率方面比最先进的算法高出 3 个百分点，在精确率方面高出 7.18 个百分点。更重要的是，通过将集成梯度用作可解释性的一层，我们表明，模型得出的皮肤电活动压力特征与现有文献之间存在很强的重叠，而当前医学研究和心理学中的最先进的压力检测系统正是基于这些文献。
