Millimeter-wave (mmWave) radar is a promising active sensing technology for Human Pose Estimation (HPE). However, its reliability is hindered by poor generalization in scenarios unseen during the model's training phase. This thesis presents a comprehensive empirical study to analyze and quantify the mmWave dimensions causing the poor generalization for HPE models. To enable this research, we introduce mmDiverse, a new large-scale dataset containing varied human movements, users, environments, and distances from the radar. Using this dataset, we evaluated two foundational models, Baseline MARS and a temporally aware version, Temporal MARS, through a series of experiments designed to isolate each dimension. The results reveal that human diversity is the most critical challenge, with model accuracy dropping by over 100% when encountering an unseen individual. Unseen movements pose the next significant challenge, where the models revert to the learned movements exposed during model training rather than generalizing to new kinematic movement patterns. Additionally, our study shows that the model's capacity to learn new kinematic patterns is enhanced by integrating model-centric techniques such as temporal modeling. This study also reveals that training in cluttered, noisy environments, combined with a target classification and tracking data pre-processing pipeline, is crucial for improving model robustness. Based on these findings, this thesis provides a set of evidence-based guidelines for developing more resilient mmWave-based HPE systems. This study concludes that building reliable applications requires prioritizing the collection of data with extensive user and movement diversity, captured across noisy and cluttered real-world environments. Furthermore, this diverse dataset should be used to train a temporal-aware model architecture incorporating a data pre-processing pipeline to mitigate generalization challenges. ... Read more