Past Projects

Establishment of Metabolomic-based Osteosarcoma Prognostic Model Using Non-Convex Kernel Models

The project aim is to establish a prognostic predictive model using non-convex kernel models to distinguish between benign and malignant tumor patients. This model will provide adjuvant information to stratify osteosarcoma patients and subsequently to guide clinical decision.

Leveraging Health Information Health Technology to Improve Communication Between Cancer Patients and Providers

The project is a collaboration between CareProgress and the University of Michigan. The project aims to develop computational algorithms to analyze the clinical and self-reported data collected by CareProgress to assess the recovery and effectiveness of treatment and health resource consumption. Improving the accuracy and reliability of the predictions on the recovery and health resource consumption by using the physiological data collected by wearable sensors, including through the creation of patient risk scores.



Machine Learning Based Human Behavior Detection and Interpretation

In this project, the project team at U-M hereafter, in collaboration with DENSO, will create computational methods, in particular deep learning algorithms, to understand and predict human behavior and affective state (distracted/focused, drowsy/active, etc.). The team will implement the above-mentioned algorithms in MATLAB and Python framework.

Non-Invasive Monitoring of Peripheral Artery Behavior via Wearable Sensors

his project continues the development of the Piezo Ring. The ring portably measures the vascular tone and reactivity of small blood vessels in the finger, which gauge a patient’s physiology and hypotension. Using computational methods, the ring predicts hypotension ranging from 9-108 minutes in advance.

This is useful for hemodialysis patients who greatly benefit from constant blood pressure monitoring, as well as in combat and critical care situations.

The Piezo Ring is easily portable, provides continuous hemodynamic monitoring, and predicts hypotension up to 108 minutes in advance. The Piezo Ring also provides more relevant data to physicians, making it a better indicator of patient physiology than current devices such as pulse oximeters.

The Impact of Sleep Quality on Symptoms, Cognition, and Functioning in SCI

While sleep disruption and disorders are common in individuals with spinal cord injury (SCI), little is known about how these disturbances impact symptoms (fatigue and pain), cognition (subjective and objective), and functioning (physical activity and social participation) in these individuals. Because sleep disturbance has been shown to be associated with poor functional outcomes (i.e., worse symptoms, poorer cognition, less physical activity, and reduced social participation) in other clinical populations such as multiple sclerosis, and these disturbances are readily treatable, there is a need to better understand these relationships in individuals with SCI.