Automatic human fall detection in fractional fourier domain for assisted living
Shengheng Liu, Zhengxin Zeng, Yimin D. Zhang, Tingting Fan, Tao Shan, Ran Tao
Abstract
Fast and accurate detection of elderly falls can significantly reduce the rate of morbidity and mortality. In the past decade, extensive research has been performed to achieve real-time fall monitoring solutions. In this paper, we consider the radar-based modality and utilize the family of fractional Fourier transform to enhance the motion Doppler signature of falls. Compare with the conventional time-frequency analysis approaches, the proposed method achieves higher signal energy concentration and thus yields improved fall detection in low signal-to-noise ratio scenarios. Experimental results are used to validate the theoretical analysis and to demonstrate the feasibility of the proposed approach.
BibTeX
@inproceedings{icassp2016_automatichumanfa,
title = {Automatic human fall detection in fractional fourier domain for assisted living},
author = {Shengheng Liu and Zhengxin Zeng and Yimin D. Zhang and Tingting Fan and Tao Shan and Ran Tao},
booktitle = {ICASSP 2016},
year = {2016}
}