Recognition of Approximate Motions of Human Based on Micro-Doppler Features

With the aging society approaching, the daily safety of the elderly has attracted more and more people’s attention, especially the elderly who live alone without anyone to take care of them. How to judge whether there is danger from the daily behavior of those who live alone and providing timely assistance is a research topic of health protection in the field of smart home care. Modern radar sensing technology and intelligent perception technology adopted in the domain of health care have started to gain significant interest, which can replace wearable sensors or cameras without feeling uncomfortable or having privacy issues. In this article, some preliminary results were presented to develop an indoor monitoring system for human activity recognition using micro-Doppler features of radar signals. The experimental campaign involved five different with some similar activities of ten subjects to be collected using a Doppler radar. A total of several texture features and numerical features are extracted from the spectrogram and time–frequency domain of measurements and applied three machine learning (ML) algorithms, such as support vector machine (SVM),
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-nearest neighbor (KNN), and decision tree (D-Tree) for the precise classification of all five different motions. The results illustrated that the SVM classifier using the radial basis function (RBF) kernel reaches the best results for all three feature sets, and the classification of mixed features can reach 93.2%, as well as 88.4% and 92.4% for numerical and texture features. The performance of SVM is 1.6% better than KNN and 3.2% better than D-Tree.