Radar-Based Human Activity Classification for Assisted Living

Author(s): Fauzia Ahmad
SourceFERMAT, Volume 24, Communication 6, Nov.-Dec., 2017


AbstractThe elderly population aged 65+ years is growing and their ratio to the population aged 15-64 is expected to reach 40% by 2030. This implies that those of working age, and, subsequently, the overall economy, will face a greater burden in supporting the aging population. In addition, the demand and trend is upward for continued independent living. As such, there is a growing interest in assisted living technologies that enable selfdependent living within homes and residences for the elderly. Remote monitoring capabilities, such as detection of falls and small changes in motor functional abilities of the elderly, will address the challenges associated with self-dependent living. This talk focuses on the radar technology and discusses the time-frequency based nonstationary signal processing techniques used to provide the local signal behavior over frequency and to detail the changes in the Doppler and micro-Doppler radar signatures over time. Features that capture the intrinsic differences in the time-frequency signatures of different gross motor activities of the elderly are identified and their performance in human activity classification is demonstrated using real data measurements. Offerings of the range information, in addition to Doppler, for classifying different motion articulations with enhanced reliability are also highlighted.

Index termsDoppler and micro-Doppler radar signatures, Assisted Living



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Radar-Based Human Activity Classification for Assisted Living









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