Dual-Path CNN-BiLSTM for mmWave-Based Human Skeletal Pose Estimation

In this article, we introduce a novel method for human skeletal joint localization using millimeter-wave (mmWave) radar, effectively overcoming the limitations of vision-based pose estimation methods, which are vulnerable to changes in lighting conditions and pose privacy concerns. The method leverages mmWave radar to generate 4-D time-series point cloud data, which is then projected onto the depth-azimuth and depth-elevation planes. This projection helps mitigate the sparsity inherent in traditional point cloud data and reduces the complexity of the machine learning model required for pose estimation. The input data structure is optimized using a sliding window technique, where consecutive frames are processed by a convolutional neural network (CNN) to extract spatial features. These features are then sorted chronologically and fed into a bi-directional long short-term memory (BiLSTM) to capture temporal features, resulting in the accurate localization of 25 skeletal joints. To validate the performance and effectiveness of the proposed method, we created a dataset comprising three body types and ten distinct actions. The experimental results demonstrate the method's outstanding human pose estimation capability.