NSF Award
2318671
Mobility scooters are an affordable and popular assistive mobility technology that is critical in facilitating the social participation of people with mobility challenges/disabilities, thereby improving their quality of life. However, the challenges of mobility scooter driving safety are evident, and mobility scooter accidents can cause serious injuries, with the potential consequence even being death.<br/>To tackle this challenge, this project builds a driving safety assessment system for mobility scooter drivers based on multi-sourced data. This system can be used at home or in clinical settings to assist doctors and rehabilitation experts. This work generates a mobility scooter safety assessment metrics framework and system that provides new usable, accurate and interpretable techniques to advance rehabilitation and tele-medicine processes for the target group of population, empowered by deep-learning-based data intelligence. The interdisciplinary project team forms synergy between Computer Science and Kinesiology and Health Promotion from two Hispanic Serving Institutions, i.e., California State Polytechnic University, Pomona and University of Texas San Antonio, in collaboration with Casa Colina Hospital and Centers for Healthcare and California State University Northridge Center of Achievement. This work introduces real-world research problems to underrepresented minority students in participating institutions, and the demonstration of research capacity expansion can be disseminated to other Minority Serving Institutions. <br/><br/>This project consists of the following research thrusts: 1) Building a longitudinal mobility scooter driving dataset collected from the mobility-disabled/challenged population with various conditions (e.g., neurologic disorders, age-related diseases, and dysfunctions); 2) Developing a mobility scooter driving safety metrics framework; 3) Designing robust, multi-modal deep learning algorithms for accurate safety assessment, employing pose-estimation and time series analysis of multi-modal data; 4) Implementing the proposed driving safety assessment system on users’ mobility scooters using customizable hardware and software, and conducting extensive testing and evaluation of the proposed system.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
