Gait characterization via pulse-Doppler radar

Falls are a major cause of injury in the elderly with almost 1/3rd of people aged 65 and more falling each year [1]. This work aims to use gait measurements from everyday living environments to estimate risk of falling and enable improved interventions. For this purpose, we consider the use of low-cost pulse-Doppler range control radar. These radars can continuously acquire data during normal activity of a person in night and day conditions and even in the presence of obstructing furniture. A short-time Fourier transform of the radar data reveals unique Doppler signatures from the torso motion and the leg swings. Two algorithms that can extract these features from the radar spectrogram are proposed in this study for estimating gait velocity and stride durations. The performance of the proposed radar system is evaluated with experimental data, which consists of 9 different walk types and a total of 27 separate tests. A high accuracy motion-capture camera system has also been used to acquire data simultaneously with the radar and provides the ground truth reference. Results indicate that the proposed radar system is a viable candidate for gait characterization and can be used to accurately track mean gait velocity, mean stride duration and stride duration variability. The gait velocity variability can also be estimated but with relatively larger error levels.

[1]  R. Lipton,et al.  Quantitative gait markers and incident fall risk in older adults. , 2009, The journals of gerontology. Series A, Biological sciences and medical sciences.

[2]  A. Waxman,et al.  Acoustic micro-Doppler radar for human gait imaging. , 2007, The Journal of the Acoustical Society of America.

[3]  Dave Tahmoush,et al.  Radar Stride Rate Extraction , 2009, 2009 13th International Machine Vision and Image Processing Conference.

[4]  C. Hornsteiner,et al.  Characterisation of human gait using a continuous-wave radar at 24 GHz , 2008 .

[5]  Chunjiang Qian,et al.  Development of a full body balance model using an artificial neural network approach , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[6]  Bhiksha Raj,et al.  Acoustic Doppler sonar for gait recogination , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[7]  A. W. M. van den Enden,et al.  Discrete Time Signal Processing , 1989 .

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

[9]  J.L. Geisheimer,et al.  A continuous-wave (CW) radar for gait analysis , 2001, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[10]  G.F. Harris,et al.  Walker-assisted gait in rehabilitation: a study of biomechanics and instrumentation , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Richard C. Waters,et al.  Mitsubishi Electric Research Laboratories, Inc. , 2000 .

[12]  Alexander Ekimov,et al.  Human motion analyses using footstep ultrasound and Doppler ultrasound. , 2008, The Journal of the Acoustical Society of America.