Competitive sprinters must train for years to perfect their physical ability and running technique in order to perform at the highest level. For beginner and intermediate athletes, progression towards better technique can be accelerated by coaching that focuses on inducing the ideal technique. One tool of coaches, concurrently bolstered by neurorehabilitation research[1], involves facilitating passive motor learning in the athlete, where their body is guided through ideal movements that they later attempt to reproduce on their own. While this guided movement alone can have little effect, combining it with intent and attention on the part of the athlete (or patient) has been shown to have positive results[2]. For this purpose, we constructed a scale model of a gait training device that could slowly guide an athlete’s legs through the ideal trajectory of a high-level sprinter during competition. Even at low speed, this could help improve training effectiveness and result in improved sprinting performance of the training athlete. In this project, we will build a small-scale prototype that simulates ankle and knee kinematics that accurately reflect those of a competitive sprinter’s during a racing event.
First, kinematic data of the ankle, hip, and knee moving parallel to the sagittal plane was extracted from a video of sprinters competing in a relay race. Next, the output of a mechanism known as a Jansen's linkage was adapted to fit the kinematic data using an algorithm that altered the lengths of each link to minimize the error between the simulated output path and the kinematic data. Once optimized link lengths were known, the device was constructed using laser-cut acrylic links and driven using a motor controlled via Arduino.
This project is similar to and inspired by a similar gait trainer developed by Sung Yul Shin in the Rewire Lab[3]. However, where that gait trainer was designed for a walking gait, this project simulates sprinting gait cycles, investigating the limits of the Jansen's linkage adaptability and exploring how mechanisms can be optimized for significantly different outputs.
Figure 1. The scaled prototype of a gait trainer device which inspired our project, designed by Sung Yul Shin working in Dr. Sulzer’s Rewire Lab[4].