FitMe: A Fitness Application for Accurate Pose Estimation Using Deep Learning

Abstract

The advancements in deep learning have brought about crucial transformations in computer vision over the past two decades. Deep convolutional networks have found many applications in building fine-tuned models for implementation in vision-related tasks. Knowledge learned by deep learning models over enormous generic datasets can be transferred to be employed for much more specific tasks. In this work, are implementing the approach to provide health benefits to people. In the present work, we develop an application which help them in performing exercises without the help of a trainer and get instant feedback about the postures. We aim to make fitness accessible to all by removing barriers such as external hardware requirements and cost-based subscriptions. In this paper, we dive deep into the technical details about the application and the exact methodologies applied for building the same. Furthermore, results are evaluated after running the application over multiple scenarios and a comparative analysis is performed.