Fall motion detection using combined range and Doppler features

Feature selection based on combined Doppler and range information improves fall detection and enables better discrimination against similar high Doppler non-rhythmic motions, such as sitting. A fall is typically characterized by an extension in range beyond that associated with sitting, which is determined by the seat horizontal depth. In this paper, we demonstrate, using time-frequency (TF) spectrograms, that range-Doppler radar plays a fundamental and important role in motion classification for assisted living applications. It reduces false alarms along with the associated cost in the unnecessary deployment of the first responders. This reduction is considered vital for the development of in-home radar monitoring and for casting it as a viable technology for aging-in-place.

[1]  Youngwook Kim,et al.  Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[2]  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.

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

[4]  Yuan He,et al.  Range-Doppler surface: a tool to analyse human target in ultra-wideband radar , 2015 .

[5]  Boualem Boashash,et al.  Radar fall detection using principal component analysis , 2016, SPIE Defense + Security.

[6]  Jeffrey M. Hausdorff,et al.  Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls , 2012, PloS one.

[7]  Guoqing Liu,et al.  Moving target feature extraction for airborne high-range resolution phased-array radar , 2001, IEEE Trans. Signal Process..

[8]  Brian D. Rigling,et al.  Micro-Range/Micro-Doppler Decomposition of Human Radar Signatures , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Hiroyoshi Yamada,et al.  Human motion detection and extraction by using FM-CW range-Doppler radar , 2014, 2014 International Symposium on Antennas and Propagation Conference Proceedings.

[10]  Carmine Clemente,et al.  'The Micro-Doppler Effect in Radar' by V.C. Chen , 2012 .

[11]  F. R. Dickey,et al.  Development of airborne moving target radar for long range surveillance , 1991 .

[12]  Jun Zhang,et al.  Range information for reducing fall false alarms in assisted living , 2016, 2016 IEEE Radar Conference (RadarConf).

[13]  Bijan G. Mobasseri,et al.  A time-frequency classifier for human gait recognition , 2009, Defense + Commercial Sensing.

[14]  Frans C. A. Groen,et al.  Feature-based human motion parameter estimation with radar , 2008 .

[15]  M. Amin.  Time-Frequency Spectrum Analysis and Estimation for Nonstationary Random-Processes , 1992 .

[16]  Yimin Zhang,et al.  Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring , 2016, IEEE Signal Processing Magazine.

[17]  Moeness G. Amin,et al.  Minimum variance time-frequency distribution kernels for signals in additive noise , 1996, IEEE Trans. Signal Process..

[18]  L. Rubenstein,et al.  The epidemiology of falls and syncope. , 2002, Clinics in geriatric medicine.

[19]  Michael F. Otero,et al.  Application of a continuous wave radar for human gait recognition , 2005, SPIE Defense + Commercial Sensing.

[20]  Tomoaki Ohtsuki,et al.  Cooperative fall detection using Doppler radar and array sensor , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[21]  Inmaculada Plaza,et al.  Challenges, issues and trends in fall detection systems , 2013, Biomedical engineering online.

[22]  Irena Orovic,et al.  A new approach for classification of human gait based on time-frequency feature representations , 2011, Signal Process..

[23]  Wenbing Tao,et al.  Radar-based fall detection based on Doppler time-frequency signatures for assisted living , 2015 .

[24]  24th European Signal Processing Conference, EUSIPCO 2016, Budapest, Hungary, August 29 - September 2, 2016 , 2016, European Signal Processing Conference.

[25]  Patrick Flandrin,et al.  Wigner-Ville spectral analysis of nonstationary processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[26]  Ram M. Narayanan,et al.  Application of Radar to Remote Patient Monitoring and Eldercare , 2015 .

[27]  Boualem Boashash,et al.  Radar fall detectors: a comparison , 2016, SPIE Defense + Security.

[28]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[29]  Dave Tahmoush,et al.  Time-integrated range-Doppler maps for visualizing and classifying radar data , 2011, 2011 IEEE RadarCon (RADAR).

[30]  Ali Cafer Gürbüz,et al.  Knowledge Exploitation for Human Micro-Doppler Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[31]  A. Enis Çetin,et al.  Signal Processing for Assisted Living: Developments and Open Problems [From the Guest Editors] , 2016, IEEE Signal Process. Mag..

[32]  Youngwook Kim,et al.  Human Detection Using Doppler Radar Based on Physical Characteristics of Targets , 2015, IEEE Geoscience and Remote Sensing Letters.