mmMotion: A Real-Time Human Motion Detection Smart Home System Based on mmWave Radar

The use of millimeter-wave radar (mmWave radar) for human motion detection is essential in the realm of smart home Internet-of-Things (IoT) scenarios due to its non-intrusive nature, privacy considerations, interactive capabilities, and cost-effectiveness. However, the existing methods fail to address the issue of low real-time detection rates. Moreover, the design of the action does not align with the requirements of smart home IoT systems, while the complexity of the algorithm leads to high computing hardware costs. To address these problems, we propose mmMotion, a novel mmWave radar-based human motion detection system capable of accurately estimating six well-designed human motions (sit down, stand up, get up, lie down, wave hands, and punch) commonly observed in smart home scenarios with cost-effectiveness and real-time processing speed. By incorporating the spatial feature of “Height”, we are the first to propose a novel feature extraction method using the “Doppler-Height-Time” (DHT) feature maps as system input for human motion detection. A combined lightweight Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture is developed to simultaneously capture spatial and temporal features, leveraging the strengths of both architectures. Furthermore, we propose a novel Real-time Classifier combined with the Static Clutter Removal (SCR) method and Duo Filter algorithm to enhance the real-time detection capability of mm-Motion. Experiment results demonstrate that mmMotion achieves an impressive offline training accuracy rate of 98.13% and a real-time motion recognition rate of 90.5% for six designated motions. Furthermore, it exhibits excellent generalization ability across different individuals and robustness to the interference of large and small actions during real-time performance testing.