We propose a novel mmWave radar-based system capable of accurately estimating six human motions (Sit down, stand up, snap, swing hands, get up, and lie down) commonly observed in smart home scenarios with cost-effectiveness and real-time processing speed. We are the first to propose the “Doppler-Height-SNR-Time” feature maps as system input for human motion estimation. We propose a novel approach that combines lightweight CNN and LSTM architectures. Our approach leverages the strengths of both models to enhance system accuracy by effectively capturing spatial and doppler velocity features using CNN while extracting temporal features related to motion continuity using LSTM. Our model achieves an impressive offline testing accuracy of 96.7% and demonstrates a 73.33% recognition rate for predefined actions in real-time scenarios, along with robustness against non-target motions in experiments.