Walker Design

Walker design encompasses a broad range of applications, from assistive mobility devices and robotic locomotion to object tracking in computer vision. Current research focuses on optimizing walker designs through data-driven approaches, employing machine learning models and genetic algorithms to improve performance metrics like stability, mass, and efficiency. These advancements are driven by the need for more effective and personalized assistive technologies, as well as the development of robust and efficient algorithms for autonomous systems. The resulting improvements in design and control algorithms have significant implications for both rehabilitation engineering and the broader field of robotics.