Performance Analysis of Classification Algorithms for Activity Recognition using Micro-Doppler Feature

Classification of different human activities using micro-Doppler data and features is considered in this study, focusing on the distinction between walking and running. 240 recordings from 2 different human subjects were collected in a series of simulations performed in the real motion data from the Carnegie Mellon University Motion Capture Database. The maximum the micro-Doppler frequency shift and the period duration are utilized as two classification criterions. Numerical results are compared against several classification techniques including the Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-nearest neighbors (KNN), Support Vector Machine(SVM) algorithms. The performance of different classifiers is discussed aiming at identifying the most appropriate features for the walking and running classification.