With 39.6% of U.S. adults obese and another 31.6% overweight fitness has never been needed more in the United States of America. Although gyms are accessible and relatively cheap, these statistics show that there are other factors still hindering citizens to workout. One common factor is the lack of knowledge. Working with a personal trainer is a great augmentation to experience in the gym, but not all people have access to this. Personal training can be very expensive, averaging 65 dollars an hour. I aim to develop an open-source app utilizing pose detection in order to substitute for a personal trainer, giving feedback on exercises performed by lifters.

My app builds on an open-source convolutional neural network framework called VideoPose3D. Once trained into a model, this framework can output a skeletal model from an image, extracting the position of detected joints. I will optimize the model's accuracy by training it with diverse exercise datasets to cater to various demographics. My app will calibrate by prompting the user to input height, weight, and goals, comparing video input from the user to a reference video showcasing exemplary form. This reference video will be displayed when selecting a given exercise so the user can learn from an ideal repetition. Real-time feedback based on the lifter’s performance in a given set will be provided, correcting form and adjusting weight based on difficulty of the repetition. To avoid overwhelming the user, the app will prioritize the most crucial feedback first, as a personal trainer would. My goal for this app is to provide assistance to the community on their fitness journeys, increasing the overall quality of their workout along with their longevity.