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Stress Detection Using Context-Aware Sensor Fusion From Wearable Devices
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DOI：10.1109/JIOT.2023.3265768.png)
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Abstract
En 中文
Wearable medical technology has become increasingly popular in recent years. One function of wearable health devices is stress detection, which relies on sensor inputs to determine a patient's mental state. This continuous, real-time monitoring can provide healthcare professionals with vital physiological data and enhance the quality of patient care. Current methods of stress detection lack: 1) robustness-wearable health sensors contain high levels of measurement noise that degrades performance and 2) adaptation-static architectures fail to adapt to changing contexts in sensing conditions. We propose to address these deficiencies with SELF-CARE, a generalized selective sensor fusion method of stress detection that employs novel techniques of context identification and ensemble machine learning. SELF-CARE uses a learning-based classifier to process sensor features and model the environmental variations in sensing conditions known as the noise context. SELF-CARE uses noise context to selectively fuse different sensor combinations across an ensemble of models to perform robust stress classification. Our findings suggest that for wrist-worn devices, sensors that measure motion are most suitable to understand noise context, while for chest-worn devices, the most suitable sensors are those that detect muscle contraction. We demonstrate SELF-CARE's state-of-the-art performance on the WESAD data set. Using wrist-based sensors, SELF-CARE achieves 86.34% and 94.12% accuracy for the 3-class and 2-class stress classification problems, respectively. For chest-based wearable sensors, SELF-CARE achieves 86.19% (3-class) and 93.68% (2-class) classification accuracy. This work demonstrates the benefits of utilizing selective, context-aware sensor fusion in mobile health sensing that can be applied broadly to Internet of Things applications.
Keywords:
Wearable computers
Anxiety disorders
Sensor fusion
Performance evaluation
Human factors
Context modeling
Sensors
Context-aware models
ensemble learning
stress detection
wearable health sensor fusion
Journal
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7.8W
