In this paper, we propose mmPose-FK, a novel millimeter wave (mmWave) radar-based pose estimation method that employs a dynamic forward kinematics (FK) approach to address the challenges posed by low resolution, specularity, and noise artifacts commonly associated with mmWave radars. These issues often result in unstable joint poses that vibrate over time, reducing the effectiveness of traditional pose estimation techniques. To overcome these limitations, we integrate the FK mechanism into the deep learning model and develop an end-to-end solution driven by data. Our comprehensive experiments using various matrices and benchmarks highlight the superior performance of mmPose-FK, especially when compared to our previous research methods. The proposed method provides more accurate pose estimation and ensures increased stability and consistency, which underscores the continuous improvement of our methodology, showcasing superior capabilities over its antecedents. Moreover, the model can output joint rotations and human bone lengths, which could be further utilized for various applications such as gait parameter analysis and height estimation. This makes mmPose-FK a highly promising solution for a wide range of applications in the field of human pose estimation and beyond.