InARMS: Individual Activity Recognition of Multiple Subjects with FMCW radar

Abstract

Human Activity Recognition (HAR) can be useful in various applications such as health monitoring, security and surveillance, and smart environments. But the majority of existing HAR methods fail to recognize more than one subject in the environment. Moreover, usually a machine learning algorithm is applied for recognition which needs access to a suitable training dataset and the necessary processing power. In this paper, a non-learning approach for recognizing human activities in multi-subject environments is proposed. For this purpose, microwave Frequency-Modulated Continuous Wave (FMCW) radar is used which is able to work unobtrusively and also does not need any adjustments in different environments. We propose mathematical and morphological operations of range-Doppler map to enable the system to recognize activities in real time with inexpensive and low-power processors. Our system also measures the distance of subjects, in addition to their activity. Performance results show that InARMS can reach 89.1% and 75.1% accuracy in an environment with one and two subjects, respectively, outperforming representative existing methods by as much as 6.4%. © 2022 IEEE.