Application of Linear Predictive Coding for Human Activity Classification Based on Micro-Doppler Signatures - Javier, Rios Jesus; Youngwook Kim (IEEE geoscience and remote sensing letters, 2014)
In this letter, classification of various human activities based on micro-Doppler signatures is studied using linear predictive coding (LPC). LPC is proposed to extract the features of micro-Doppler that are mixtures of different frequencies. The use of LPC can not only decrease the time frame required to capture the Doppler signature of human motion but can also reduce the computational time cost for extracting its features, which makes real-time processing feasible. The measured data of 12 human subjects performing seven different activities using a Doppler radar are used. These activities include running, walking, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, and sitting still. A support vector machine is then trained using the output of LPC to classify the activities. Multiclass classification is implemented using a one-versus-one decision structure. The resulting classification accuracy is found to be over 85%. The effects of the number of LPC coefficients and the size of the sliding time window, as well as the decision time-frame size used in the extraction of micro-Doppler signatures, are also discussed.