Presentation + Paper
13 June 2023 Continuous human activity recognition using a MIMO radar for transitional motion analysis
John Kobak, Bennett J. Richman, LaJuan Washington Jr., Syed A. Hamza
Author Affiliations +
Abstract
The prompt and accurate recognition of Continuous Human Activity x(CHAR) is critical in identifying and responding to health events, particularly fall risk assessment. In this paper, we examine a multi-antenna radar system that can process radar data returns for multiple individuals in an indoor setting, enabling CHAR for multiple subjects. This requires combining spatial and temporal signal processing techniques through micro-Doppler (MD) analysis and high-resolution receive beamforming. We employ delay and sum beamforming to capture MD signatures at three different directions of observation. As MD images may contain multiple activities, we segment the three MD signatures using an STA/LTA algorithm. MD segmentation ensures that each MD segment represents a single human motion activity. Finally, the segmented MD image is resized and processed through a convolutional neural network (CNN) to classify motion against each MD segment.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John Kobak, Bennett J. Richman, LaJuan Washington Jr., and Syed A. Hamza "Continuous human activity recognition using a MIMO radar for transitional motion analysis", Proc. SPIE 12522, Big Data V: Learning, Analytics, and Applications , 125220D (13 June 2023); https://doi.org/10.1117/12.2663714
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KEYWORDS
Radar

Image segmentation

Spatial filtering

Detection and tracking algorithms

Tunable filters

Matrices

Phased arrays

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