In real life applications, it is crucial to monitor the different kinds of human activity without interfering with their regular occupations. Contactless physiological monitoring using radars is a valuable tool, but typically it is performed when the human subjects are immobile. This paper analyzes single channel Continuous Wave (CW) Doppler radar signals in relation to three levels of human activity: I) Sedentary and still, ii) Sedentary and moving and iii) Walking. A combination of computational intelligence techniques (GammaTest, neural networks, random forest and genetic algorithms) was used for assessing the predictive ability of 43 features derived from the radar return signal, as well as of subsets of them, which were composed of highly predictive attributes. It is shown that with about one half the number of attributes it is possible to achieve high levels of classification accuracy, in some cases improving false negative ratios. While several attributes were completely irrelevant and noisy, others were required by discriminating each of the classes. There are attributes required by certain classes in particular and there are others associated to the distinction of classes with subtle differences.

Extreme Learning Machines, Feature Selection., GammaTest, Genetic algorithms, Multilayer Perceptron, Random Forest
2018 International Joint Conference on Neural Networks, IJCNN 2018
Department of Systems and Computer Engineering

Valdes, J.J. (Julio J.), Baird, Z. (Zachary), Rajan, S, & Bolic, M. (Miodrag). (2018). Single Channel Continuous Wave Doppler Radar for Differentiating Types of Human Activity. In Proceedings of the International Joint Conference on Neural Networks. doi:10.1109/IJCNN.2018.8489391