Stress Monitoring and Detection

Stress and challenges associated with stress management are prevalent problems of modern life. Many physical and mental health problems are driven by or escalate with the degree of stress. Stress has harmful effects on those who suffer from mental and physical health problems. Therefore a comprehensive study of stress and its effect is an important research topic in the mobile health domain. Our research lies at the intersection of sensor systems and machine learning, in which we research methods of detecting stress in real-life settings. We use wearable sensor systems to capture bio-markers of stress and design and develop machine learning algorithms for stress detection and classification. Our research aims to develop tools, methodologies, and algorithms for comprehensive approaches to stress detection and to invent smart interventions strategies to promote the well-being of individuals.

Embedded Machine Intelligence Lab
Embedded Machine Intelligence Lab
Research Lab

The current focus of our research in the Embedded Machine Intelligence Lab (EMIL) is on design, development, and validation of algorithms, tools, and technologies that enhance utilization and large-scale adoption of medical embedded systems. To validate and refine the new technology, we conduct clinical studies involving patients with heart failure, diabetes, cancer, visual impairment, and gait difficulties. Clinical studies are conducted in collaboration with partners from Elson S. Floyd College of Medicine, College of Nursing, College of Pharmacy, College of Education, and College of Agricultural, Human, and Natural Resource Science at WSU as well as our collaborators at Pullman Regional Hospital, UCLA School of Medicine, UCLA Stein Eye Institute, UC-Irvine Nursing Science, and Memorial Sloan Kettering Cancer Center (MSKCC). This end-to-end approach results in innovative, evidence-based and cost-conscious solutions for patients, doctors and medical centers.