Research project title: Machine learning applications for gait signatures in patients with peripheral artery disease
Grant award: University of Nebraska Collaboration Initiative Grant. This project has been funded for a two-year total of $149,405.
Project description: In this project we propose a novel approach for the detection and monitoring of peripheral artery disease (PAD) progression using wearable devices and machine learning to analyze human gait. The long-term goal of this project is to use wearable devices in concert with machine learning to improve the diagnosis and treatment of functional problems in patients with PAD outside the clinical setting.
- Basheer Qolomany, assistant professor of computer science, Department of Cyber Systems, University of Nebraska at Kearney
- Sara Myers, associate professor, Department of Biomechanics, University of Nebraska at Omaha
- Fadi Alsaleem, assistant professor of architectural engineering, University of Nebraska-Lincoln
- Iraklis Pipinos, professor of vascular surgery, University of Nebraska Medical Center
- Jianghu Dong, assistant professor, Department of Biostatistics, University of Nebraska Medical Center
Education: Ph.D. in computer science, Western Michigan University, 2018
Years at UNK: 2
Courses taught: CYBR 101: Python for Analytics; CYBR 140: The Internet Explained; CYBR 150: Object-Oriented Programming; CYBR 405: Interactive Web Application Development; CYBR 460: Virtualization Essentials
Area of research/specialization: Deep learning in support of smart services and disease progression