Mental Health

Embedded Machine Intelligence Lab
Last updated on Feb 20, 2024
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.

Publications

Stress Monitoring in Free-Living Environments
We present a data-driven approach for stress detection based on convolutional neural networks while addressing the problems of the best sensor channel and the lack of knowledge about stress episodes.
IEEE Journal of Biomedical and Health Informatics (J-BHI).
PDF Code Dataset Project
Stress Monitoring in Free-Living Environments
ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting
A multi-modal dataset for stress and alcohol relapse collected in real world settings.
IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2022
PDF Project
ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting
Stressalyzer: Convolutional Neural Network Framework for Personalized Stress Classification
A stress classification and personalization framework based on Convolutional Neural Networks.
IEEE Engineering in Medicine and Biology Conference (EMBC), 2022
PDF Code Dataset Project
Stressalyzer: Convolutional Neural Network Framework for Personalized Stress Classification
Comparing the Predictability of Sensor Modalities to Detect Stress from Wearable Sensor Data
IEEE Consumer Communications & Networking Conference (ICCNC), 2022.
PDF Project
Comparing the Predictability of Sensor Modalities to Detect Stress from Wearable Sensor Data
Stress Classification and Personalization: Getting the most out of the least
17th IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN'21)
PDF Project
Stress Classification and Personalization: Getting the most out of the least
Associations between Physiological Signals Captured using Wearable Sensors and Self-Reported Outcomes among Patients in AUD Recovery: Development and Usability Study
Journal of Medical Internet Research (JMIR)
PDF Dataset Project DOI
Associations between Physiological Signals Captured using Wearable Sensors and Self-Reported Outcomes among Patients in AUD Recovery: Development and Usability Study
Mobile Health for Alcohol Recovery and Relapse Prevention
IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
PDF Project
Mobile Health for Alcohol Recovery and Relapse Prevention