Radar placement for fall detection: Signature and performance

Two popular mounting positions of Doppler radar for human fall detection are in the ceiling center and at the torso level. This paper examines the fall signatures observed by a Doppler radar at the two positions and evaluates their consistencies with respect to the fall directions and locations. The complementary characteristics of the fall signatures motivate the integration of the features from the ceiling mounted and torso level radars to improve the fall detection performance. Experimental results using the data collected of an elderly at a senior residence apartment for almost a year support our studies and the benefit of using both radars. The false alarm rate is reduced by a factor of 10 at 100% detection rate when compared to using a ceiling radar alone.

[1]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[2]  Liang Liu,et al.  Doppler radar sensor positioning in a fall detection system , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Branka Jokanovic,et al.  Multi-window time–frequency signature reconstruction from undersampled continuous-wave radar measurements for fall detection , 2015 .

[4]  Aly E. Fathy,et al.  Phase based motion characteristics measurement for fall detection by using stepped-frequency continuous wave radar , 2016, 2016 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS).

[5]  James C. Bezdek,et al.  An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm , 2012, IEEE Transactions on Knowledge and Data Engineering.

[6]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[7]  Boualem Boashash,et al.  High-resolution time-frequency distributions for fall detection , 2015, Defense + Security Symposium.

[8]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[9]  Liang Liu,et al.  An automatic in-home fall detection system using Doppler radar signatures , 2016, J. Ambient Intell. Smart Environ..

[10]  Bahri Cagliyan,et al.  Micro-Doppler-Based Human Activity Classification Using the Mote-Scale BumbleBee Radar , 2015, IEEE Geoscience and Remote Sensing Letters.

[11]  Marjorie Skubic,et al.  Doppler Radar Fall Activity Detection Using the Wavelet Transform , 2015, IEEE Transactions on Biomedical Engineering.

[12]  Liang Liu,et al.  Automatic fall detection based on Doppler radar motion signature , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[13]  B. Silverman,et al.  The Stationary Wavelet Transform and some Statistical Applications , 1995 .

[14]  Moeness G. Amin,et al.  Fall detection and classifications based on time-scale radar signal characteristics , 2014, Defense + Security Symposium.

[15]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[16]  Paul Leroux,et al.  Analysis of a fall detection radar placed on the ceiling and wall , 2014, 2014 Asia-Pacific Microwave Conference.

[17]  Seung Hong Hong,et al.  Comparison between short time Fourier and wavelet transform for feature extraction of heart sound , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[18]  I. Daubechies.  Ten Lectures on Wavelets , 1992 .

[19]  Ohtsuki Tomoaki,et al.  Cooperative Fall Detection Using Doppler Radar and Array Sensor , 2013 .

[20]  P. Leroux,et al.  SFCW microwave radar for in-door fall detection , 2012, 2012 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS).

[21]  Marco Mercuri,et al.  Embedded DSP-Based Telehealth Radar System for Remote In-Door Fall Detection , 2015, IEEE Journal of Biomedical and Health Informatics.