Computationally efficient classification of human transport mode using micro-doppler signatures

Gait-based person and gender recognition using micro-doppler signatures

“…The ultrasonic hardware was connected to a computer via a RS232 link, through which the PIC transmits the raw data to the PC, which is then streamed to disk via Matlab c . More details on the hardware are given in [11].…”

“…The most common applications are: detecting presence, counting, tracking and identifying individuals [1]- [4]. In addition a few studies on face recognition [5]- [7], one handed gesture recognition [8], fall detections [9], [10] or mode of transport classification [11] have also been reported. A number of features set the different systems apart, such as the transmission mode of the wave, the type of wave used for microdoppler (acoustic or electromagnetic) and finally the data analysis methods.…”

“…A number of features set the different systems apart, such as the transmission mode of the wave, the type of wave used for microdoppler (acoustic or electromagnetic) and finally the data analysis methods. There are three modes of transmission used, with the two most common being the continuous wave (CW) sonar at 40kHz [4], [6], [8], [11] or CW RF at 10.5GHz [2], [3], and the Pulse Doppler RF [9], [10] with a carrier at 5.8GHz and pulse modulated at a lower frequency. The third mode uses chirps, sweeping frequencies from 25kHz to 100kHz [5], [7].…”

“…The most common data analysis methods start with a Fast Fourier Transform (FFT) or a Short-Time Fourier Transform (STFT) to process the data and then a classifier is used for pattern recognition. A broad range of classifiers are used: linear [3], neural network [5], standard deviation+linear [11], Bayesian [4], [6], Gaussian Mixture Model (GMM, [8]), Maximum Correlation Coefficient (MCC, [7]), Support Vector Machine (SVM, [10]), and k-Nearest Neighbour (kNN, [10]). The proposed solution uses a CW sonar at 40kHz with bandpass sampling and a novel data analysis method which will be described below.…”

“…Zhang and Andreou reported 90% success rate for identifying 8 walking subjects [12]. In this work, the ultrasonic module (US module) [11], shown in Fig. 1, was used to obtain mD signatures of walking subjects to identify individuals with an accuracy reaching as high as 100% for one subject and to provide gender discrimination with an accuracy of 92.4%.…”