A Resilient and Hierarchical IoT-Based Solution for Stress Monitoring in Everyday Settings

The conventional mental healthcare regime often follows a symptom-focused and episodic approach in a noncontinuous manner, wherein the individual discretely records their biomarker levels or vital signs for a short period prior to a subsequent doctor's visit. Recognizing that each individual is unique and requires continuous stress monitoring and personally tailored treatment, we propose a holistic hybrid edge-cloud Wearable Internet of Things (WIoT)-based online stress monitoring solution to address the above needs. To eliminate the latency associated with cloud access, appropriate edge models-spiking neural network (SNN), Conditionally Parameterized Convolutions (CondConv), and support vector machine (SVM)-are trained, enabling low-energy real-time stress assessment near the subjects on the spot. This work leverages design-space exploration for the purpose of optimizing the performance and energy efficiency of machine learning inference at the edge. The cloud exploits a novel multimodal matching network model that outperforms six state-of-the-art stress recognition algorithms by 2%-7% in terms of accuracy. An offloading decision process is formulated to strike the right balance between accuracy, latency, and energy. By addressing the interplay of edge-cloud, the proposed hierarchical solution leads to a reduction of 77.89% in response time and 78.56% in energy consumption with only a 7.6% drop in accuracy compared to the Internet of Things (IoT)-Cloud scheme, and it achieves a 5.8% increase in accuracy on average compared to the IoT-Edge scheme.