The focus of our research at WSU EPSL is on embedded systems design and technologies, remote and intelligent health monitoring, and machine learning theory and applications. We are interested in developing these technologies with an emphasis on accuracy, user experience, autonomy, seamlessness, and robustness.
Computational algorithms need to be reconfigured (i.e., retrained) upon any changes in configuration of wearable technologies, such as addition/removal of a sensor to/from the network, displacement/misplacement/mis-orientation of the sensors, etc. Re-training of these algorithms requires collecting sufficient amount of labeled training data, a time consuming, labor-intensive, and expensive process that has been identified as a major barrier to personalized and precision medicine. In this project, we investigate development of multi-view learning algorithms that enable the vision of computationally autonomous and result in highly sustainable and scalable wearables of the future. read more..
|Power-Aware System Design
The stringent constrained resources available on tiny sensor nodes introduce a number of challenges regarding accuracy, power-efficiency, user-comfort, and security. These design considerations, however, often impose conflicting requirements. Thus, a comprehensive research approach to design future medical embedded systems and corresponding optimizations at different levels must consider these interdependent and conflicting requirements. We research methods of optimizing medical embedded systems for power-efficiency while taking into account other performance metrics. The goal is to develop tools, methodologies, and algorithms towards comprehensive approaches to address cross-layer optimization issues related to power, performance, user-comfort, and security. read more..
We develop novel approaches for reliable gait monitoring and investigate applications of wearable-base gait monitoring in various populations. The utility of wearable sensors for continuous gait monitoring has grown substantially, enabling novel applications on mobility assessment in healthcare. Existing approaches for gait monitoring rely on predefined or experimentally tuned platform parameters and are often platform-specific, parameter-sensitive, and unreliable in noisy environments. To address these challenges, we investigate platform-agnostic and reconfigurable computational approaches to gait monitoring, step counting, mobility assessment, and related problems. read more..
Peripheral Edema is a condition characterized by an excess of watery fluid collecting in the cavities or tissues of the body. Ankle edema is a common symptom in many chronic diseases such as heart failure, liver disease, chronic obstructive pulmonary disease (COPD), heart disease, diabetes, and renal failure. Monitoring Edema helps caregivers and patients to understand the state of the disease as well as the effectiveness of treatments. SmartSock is a wearable and context-aware prototype for intelligent in-home monitoring of edema. SmartSock is powered by advanced machine learning, signal processing, and correlation techniques to provide real-time, reliable, and context-rich information in remote settings. read more..