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

Moeness G. Amin; Arun Ravisankar; Ronny G. Guendel

doi:10.1117/12.2519984

13 May 2019 RF sensing for continuous monitoring of human activities for home consumer applications

Moeness G. Amin, Arun Ravisankar, Ronny G. Guendel

Proceedings Volume 10989, Big Data: Learning, Analytics, and Applications; 1098907 (2019) https://doi.org/10.1117/12.2519984
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

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.

Citation Download Citation

Moeness G. Amin, Arun Ravisankar, and Ronny G. Guendel "RF sensing for continuous monitoring of human activities for home consumer applications", Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 1098907 (13 May 2019); https://doi.org/10.1117/12.2519984

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