The field of human pose estimation and motion tracking is rapidly advancing, with a focus on developing low-cost, privacy-aware, and accurate solutions. Researchers are exploring the use of various sensors, such as insole-type sensors, pressure sensors, and inertial measurement units (IMUs), to capture human motion and estimate 3D pose. The integration of machine learning models, particularly deep learning techniques, is improving the accuracy and robustness of these systems. Furthermore, the development of novel representations, such as polar coordinate-based 2D pose priors, and the incorporation of physics-based optimization schemes are enhancing the estimation of global motion and local poses. Notable papers in this area include P2P-Insole, which presents a low-cost approach for estimating 3D human skeletal data using insole-type sensors, and TxP, which proposes a bidirectional Text×Pressure model for reciprocal generation of ground pressure dynamics and activity descriptions. Additionally, the Progressive Inertial Poser method demonstrates accurate full-body pose estimation using only three IMU sensors, making it a promising solution for virtual reality applications.