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

Abstract

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.