The University of Arizona

Janet Roveda

Bahram Jalali

Kavan Hazeli

Linda Powers

Siteng Chen

Stuart Quan

Ao Li

A life-assisting system to enhance the quality of life for people with visual impairment. The system integrates state-of-the-art sensing and sensory stimulation with light detection and ranging (LiDAR), machine learning, and advanced haptic navigation to provide the visually impaired population with a real-time haptic map of the three-dimensional environment and auditory descriptions of objects within that environment. By allowing the visually impaired population to sense objects in the surrounding environment through haptic and auditory feedback, the disclosed system has the potential to promote individual independence, reduce anxiety and stress, facilitate access to educational and employment opportunities, and reduce social and economic gaps. To provide those benefits, the system uses multi-modal learning to convert higher dimensional (e.g., four-dimensional) scene data into lower dimensional (e.g., one-dimensional) auditory data and (e.g., two- or three-dimensional) haptic data.

Converting spatial information to haptic and auditory feedback

Systems and methods detect cortical arousal events from a single time-varying ECG signal that is obtained via single-lead ECG. A pre-trained deep neural network transforms the ECG signal into a sequence of cortical-arousal probabilities. The deep neural network includes an inception module, a residual neural network, and a long short-term memory neural network to identify structure in the ECG signal that distinguishes periods of cortical arousal from periods without cortical arousal.

Neural-network based electrocardiogramy monitor and associated methods

Growing community of inventors

TUCSON, AZ, United States of America

Janet Roveda