Analysis of a person's movement provides important information about his or her health status. This analysis can be performed with wearable devices or with non-contact technologies. These latter in particular are of some interest, since the subject is free to move and the analysis of the movement is realistic. Despite being designed for other purposes, automotive mmWaves radars represent a powerful low-cost technology for detecting people's movements without contact and finds interesting applications as a support for home monitoring of health conditions. In this paper it is shown how to exploit commercial radars to distinguish with high precision the way of walking of a subject and the position of his hands during the activity carried out. The application of Principal Component Analysis (PCA) for feature extraction from raw data is considered, together with supervised machine learning algorithms for the actual classification of the various activities carried out during the experiments.