Research Projects

In this proposal, the PIs develop novel theoretical foundations and scalable algorithms for tensor decomposition and apply them to some major Big Data tasks such as noise removal, data imputation, information integration and dimension reduction. To evaluate the performance of the proposed tensor methods, they will be applied to early detection of sepsis, which represents a spectrum of complex problems in medicine and science.

The proposed system will accurately predict or detect the onset of severe cardiac events (e.g., hemodynamic instability) prior to complications experienced by the driver in order to ensure driver safety.

We have developed wound segmentation and deep neural network models that can accurately predict infection and wound healing. The image segmentation methods provide an accurate “hands-free” measurement of wound surface area. Our objective here is to refine our algorithms and test the suitability of image analysis as a biomarker for healing or infection. We will do this by performing image analyses of DFU treated by total contact cast (TCC) and by comparing the image features to those not treated by TCC. Our working hypothesis is there will be unique features from image analysis that can accurately predict infection or healing beyond surface area changes over time, known clinical predictors (Brownrigg, Hinchliffe et al. 2016) or candidate biomarkers.

Architecture design, test and validate image/video processing and machine learning method to detect and understand human’s affective state by analyzing face images captured by an in-vehicle camera. Specifically, it includes:

1. Real-time face detection/localization and automatic annotation.
2. Dynamic face localization through a sequence of images recorded over time. Spatial-temporal feature extraction to understanding facial expression and motion
3. Multi-model fusion to reason and understand human’s behavior and state, especially for the driver drowsiness application.

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.

Among other image modalities used for TBI assessment, a number of recent studies reported that hand-held optic nerve ultrasound (ONUS) may help identify elevated intracranial pressure (ICP). In the largest study of ICP prediction using ONUS to date, we found that optic nerve sheath diameter can accurately predict ICP > 25 mmHg in a mixed population of patients in a neurosurgical ICU. Our preliminary work has demonstrated the feasibility of fully automated algorithms to measure optic nerve sheath diameter. In this project we will further develop these algorithms to predict ICP from measuring the optic nerve sheath diameter.

This project aims to create novel computational methodologies that synthesize and integrate longitudinal electronic health data streams for real-time and continuous health status monitoring and early detection of disease. We will utilize these technologies to address the problem of monitoring patients with lung disease to detect the presence of the acute respiratory distress syndrome (ARDS) at early stages. The project combines the capabilities of two emerging fields of machine learning, privileged learning and learning from uncertain data, both highly relevant to healthcare applications. While the current project focuses on improving diagnosis of ARDS, the proposed learning methods will generalize across healthcare settings, allowing for better characterization of patient health status in both in-hospital and in-home settings via portable electronic monitoring devices.

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. In this study, we will continuously assess objective autonomic nervous system and physical activity, and will collect self-reported symptoms at five intervals throughout each day for 175 individuals with SCI. In addition, participants will complete an internet-based daily diary that consists of brief questionnaires on symptoms and functional outcomes. We will also conduct a standardized neuropsychological exam at the end of the study that evaluates cognitive domains known to be impacted by poor sleep. The use of these multiple methods will increase measurement reliability and elucidate the role that sleep quality plays in day to day functioning.