Multiple joint-variable domains recognition of human motion

Radar has been successfully employed for classifying human motions in defense, security and civilian applications, and has emerged to potentially become a technology of choice in the healthcare industry, specifically in what pertains to assisted living. Due to the relationship between Doppler frequency and motion kinematics, the time-frequency domain has been traditionally used to analyze radar signals of human gross-motor activities. Towards improving motion classification, this paper incorporates three domains, namely, time-frequency, time-range, and range-Doppler domains. Features from each domain are extracted using deep neural network that is based on stacked auto-encoders. Final decision is made by combining the classification outcomes. Experimental results demonstrate that certain domains are more favorable than others in recognizing specific motion articulations, thus reinforcing the merits of multi-domain motion classifications.