Physics-Aware Deep Learning for RF-Based Gait Analysis and Sign Language Recognition
. In recent years, advances in machine learning, parallelization and the speed of graphics processing units (GPUs), combined with the availability of open, easily accessible implementations, have brought deep neural networks (DNNs) to the forefront of research in many fields. Likewise, deep learning has offered significant performance gains in the classification of radar micro-Doppler signatures, paving the way for new civilian applications of RF technologies that require a greater ability to recognize a larger number of classes that are similar in nature. However, RF data has a fundamentally different phenomenology from other application areas of deep learning, such as computer vision or natural language processing. The signal received from radar is a complex time-series, whose amplitude and phase are related to the physics of electromagnetic scattering and kinematics of target motion. Radar data oftentimes undergoes a number of filtering and analysis stages before being presented to a DNN as two- or three-dimensional data. Thus, DNN approaches, popular in other fields, do not necessarily translate to the RF domain and their benefits must be separately reassessed. A related challenge, which further necessitates the exploitation of a physics-based, knowledge-aided signal processing approach to DNN design and training, is the small amount of measured data that is available in RF applications.
This talk will discuss the unique aspects of application of deep learning to radar micro-Doppler classification, focusing on the problem of DNN training under low sample support. Generative Adversarial Networks (GANs) have achieved great success in synthetic data generation; however, we show how in the case of radar micro-Doppler signatures, misleading signatures with poor kinematic fidelity can also be generated and degrade classification performance. Methods for improving the quality of synthetic signatures generated through adversarial learning are discussed. Results and benefits for two case studies are considered: biomedical gait analysis and recognition of American Sign Language. Linguistic and kinematic insights into the application of deep learning as well as ways in which machine learning and radar signal processing can illuminate linguistics study via RF sensing are presented.
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- Date: 01 Sep 2021
- Time: 12:00 PM to 01:00 PM
- All times are (GMT-05:00) US/Eastern
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- Atlanta, Georgia
- United States
- Starts 09 August 2021 09:00 AM
- Ends 01 September 2021 05:00 PM
- All times are (GMT-05:00) US/Eastern
- No Admission Charge
Speakers
Dr. Gurbuz of University of Alabama
Physics-Aware Deep Learning for RF-Based Gait Analysis and Sign Language Recognition
Biography:
Sevgi Zubeyde Gurbuz received her B.S. and M.Eng. degrees from the Massachusetts Institute of Technology, Cambridge, in 1998 and 2000, respectively, and her Ph.D. degree from the Georgia Institute of Technology, Atlanta, in 2009, all in electrical engineering. She served in the U.S. Air Force as a radar-signal processing research engineer stationed at the Air Force Research Laboratory (AFRL), Rome, New York, from 2000 to 2004. Afterwards, she was a Senior Research Scientist at the TUBITAK Space Technologies Research Institute and faculty at the TOBB University of Economics and Technology in Ankara, Turkey. She is currently an assistant professor of electrical and computer engineering at the University of Alabama, Tuscaloosa. Her research is focused on statistical signal processing and machine learning in the design of radar-enabled cyber-physical systems for human ambient intelligence, especially as applied to remote health monitoring, gait analysis, automotive autonomy, human-computer interaction, and sign language recognition. A senior member of the IEEE, she is the recipient of the 2020 SPIE Defense+Commercial Sensing (DCS) Rising Researcher Award, and 2019 IEEE Harry Rowe Mimno Award. She is the author of over 90 publications, including a patent application and 6 book chapters related to radar-based human motion recognition, and is editor of a new book entitled “Deep Neural Network Design for Radar Applications” (IET, Dec. 2020)
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