The field of biomechanical analysis and fall detection is rapidly advancing, with a focus on developing innovative and accessible tools for clinical practice. Recent developments have centered around the use of handheld smartphone technology and machine learning algorithms to measure movement and detect falls. These advancements have the potential to enable more sensitive outcome measures and earlier identification of impairment, ultimately improving patient care. Notably, research has demonstrated the effectiveness of using convolutional neural networks and multi-sensor fusion to estimate age from gait patterns, with high accuracy rates achieved in large-scale experiments. Furthermore, the introduction of new datasets, such as AthleticsPose, has facilitated the evaluation of monocular 3D pose estimation models in sports analysis, highlighting the importance of training on authentic sports motion data. Some noteworthy papers in this area include: The Portable Biomechanics Laboratory, which presents a validated method for measuring whole-body kinematics from handheld smartphone video. The Privacy-Preserving Multi-Stage Fall Detection Framework, which proposes a framework for detecting falls using semi-supervised federated learning and robotic vision confirmation, achieving a highly reliable performance with an overall accuracy of 99.99%. The Data-Driven Meta-Analysis and Public-Dataset Evaluation for Sensor-Based Gait Age Estimation, which establishes solid performance baselines and practical guidelines for reducing gait-age error below three years in real-world scenarios. The AthleticsPose dataset, which provides a valuable resource for the research community and clarifies the potential and practical limitations of using monocular 3D pose estimation for sports motion analysis.