PoseBuddy : Pose Estimation Workout Mobile Application

The sedentary lifestyle of modern people is becoming more and more common, and a lack of physical activity is a risk factor for many diseases. Therefore, this project introduces a mobile application that works on iOS and Android, PoseBuddy, through computer vision, provides interactive experiences, personalized workout plans, and competition through connections with others. It allows users to exercise anytime and anywhere with fragmented time without the limitation of venues. PoseBuddy forms an interactive mode that obtains users’ pose data through the mobile device’s front camera, inputting to a high-accuracy human pose estimation model provided by TensorFlow, MoveNet. Afterwards, it provides real-time audio feedback during exercise after being verified by the accuracy validation algorithm. The real-time inputs from the camera feed can be captured and processed asynchronously by the system, allowing users to know in real-time if they are carrying out the workout correctly. Additionally, PoseBuddy is also a sports platform that allows users to post their experiences after exercising and making friends.