Mr. Arun Ravisankar Profile

Arun Ravisankar

Research Engineer at Comcast Corp

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Proceedings Article | 13 May 2019 Paper

RF sensing for continuous monitoring of human activities for home consumer applications

Moeness Amin, Arun Ravisankar, Ronny Guendel

Proceedings Volume 10989, 1098907 (2019) https://doi.org/10.1117/12.2519984

KEYWORDS: Radar, Radon transform, Image filtering, Sensors, Doppler effect, Control systems, Cameras, Medical imaging, Motion detection, Signal detection

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Radar for indoor monitoring is an emerging area of research and development, covering and supporting different health and wellbeing applications of smart homes, assisted living, and medical diagnosis. We report on a successful RF sensing system for home monitoring applications. The system recognizes Activities of Daily Living (ADL) and detects unique motion characteristics, using data processing and training algorithms. We also examine the challenges of continuously monitoring various human activities which can be categorized into translation motions (active mode) and in-place motions (resting mode). We use the range-map, offered by a range-Doppler radar, to obtain the transition time between these two categories, characterized by changing and constant range values, respectively. This is achieved using the Radon transform that identifies straight lines of different slopes in the range-map image. Over the in-place motion time intervals, where activities have insignificant or negligible range swath, power threshold of the radar return micro-Doppler signatures, which is employed to define the time-spans of individual activities with insignificant or negligible range swath. Finding both the transition times and the time-spans of the different motions leads to improved classifications, as it avoids decisions rendered over time windows covering mixed activities.

Proceedings Article | 14 May 2018 Paper

Realization of radar-based fall detection using spectrograms

Baris Erol, Mark Francisco, Arun Ravisankar, Moeness Amin

Proceedings Volume 10658, 106580B (2018) https://doi.org/10.1117/12.2309817

KEYWORDS: Radar, Doppler effect

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Radar has emerged as a leading technology supporting large sectors of commerce, defense and security. Enabled by the advent of small, low-cost solid-state and software-defined radar technologies, new radar applications involving cognitive radar, medical and biometric radar, passive radar, and automotive radar have been made possible. In this paper, we examine redundancy in human motion signatures along the data and short-time Fourier transform (STFT) parameters. With an "eye" on a final product, we evaluate the effect of reduced sampling along slow-time on classification performance. The goal is to determine the degree of data down-sampling that can be tolerated without compromising feature extraction or significantly impeding motion classifications. We search for the optimum STFT parameters that provide the best classification performance for the given radar measurements and gain an understanding of their respective nominal range values.

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