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

Long-term stress is a global health concern because it impacts our physical and mental health. The emergence of Internet of Things (IoT) and Artificial Intelligence (AI) makes stress monitoring and treatment more accessible compared to today’s physician-centered healthcare system. However, existing solutions either fail to incorporate IoT technology or are not cost-effective. We propose a resilient, hierarchical IoT-based solution for stress monitoring to tackle the above problems. Multimodal data was collected from wearable sensors and underwent preprocessing, feature extraction, and multiple imputation. We applied three feature-selection methods prior to lightweight SVM classification at the edge layer, and utilized a CNN and a matching network model in the cloud layer. We obtained an accuracy of 86.7347% and an F1 score of 0.8725 at the edge using only 10 features selected based on the Fisher score. An accuracy of 98.9247% and an F1 score of 0.9876 was achieved by a matching network model based on electrocardiogram (ECG) data. The trade-off between the communication cost from the edge to the cloud and the overall accuracy was evaluated. Our hierarchical-IoT solution for stress-level evaluation provides insights into the potentiality of IoT and AI technology-based eHealth solutions.