Transformer-Sequential-Based Learning for Continuous HMR with High Similarity using mmWave FMCW Radar

Doppler-radar-based continuous human motion recognition (CHMR) recently has attracted extensive attention, which is a favorable choice for privacy and personal security. Existing results of CHMR using mmWave FMCW Radar not consider the continuous human motion with the high similarity problem. In this paper, we proposed a new CHMR algorithm with the consideration of the high similarity (HS), is namely CHMR-HS, by using the modified Transformer-sequential-decoder model, such that our Transformer-sequential-decoder model can recognize the continuous human action with the high similarity. The experimental results show that the accuracy of our proposed CHMR-HS scheme is 94.5%, is better than that of existing CHMR schemes.