AI with Robotics for leg support to skiers and snowboarders

Abstract

In general, Robotics is the area concerned with the linking of perception to action, and AI must have a central role in Robotics if the association is to be intelligent. Skiing and Snowboarding are famous winter games worldwide, enjoyed by participants of all ages and skill levels. Leg dominance has been recounted as a probable risk factor in downhill skiers for lower-limb injuries. Furthermore, snowboarders are more likely to injure their ankles than alpine skiers. To overcome these issues, in this paper, the Artificial Intelligence assisted Statistical model (AIASM) has been proposed to the smart robotic supporting leg for skiers and snowboarders. This paper introduces the concept and study of a robotic modular leg (RML) system with a reduced degree of freedom (DOF). The RML gives a perspective on physics that uses dynamic skiing methods and strategies to produce functional ski movements. Kinematic and dynamic models for the leg system are developed and used for modeling tendency, angle, and measurement, unweighting technique to create balanced and realistic curvature turns and peaks. The experimental results show that the suggested system has a performance rate of 95.31% with different ski movements at various intervals, curves, diameters, and peak shapes for tracking the desired footpath.