Physical Exercise Form Correction Using Neural Networks
Cristian Militaru, Maria-Denisa Militaru, Kuderna–Iulian Benţa · Companion Publication of the 2020 International Conference on Multimodal Interaction · 2020
Monitoring and correcting the posture during physical exercises can be a challenging task, especially for beginners that do not have a personal trainer. Recently, successful mobile applications in this domain were launched on the market, but we are unable to find prior studies that are general-purpose and able to run on commodity hardware (smartphones). Our work focuses on static exercises (e.g. Plank and Holding Squat). We create a dataset of 2400 images. The main technical challenge is achieving high accuracy for as many circumstances as possible. We propose a solution that relies on Convolutional Neural Networks to classify images into: correct, hips too low or hips too high. The Neural Network is used in a mobile application that provides live feedback for posture correction. We discuss limitations of the solution and ways to overcome them.