Week 0 - Introduction

Hello, welcome to my first senior project blog! My vision for this project is to develop an app to perform as a personal trainer, aiding lifters to have agency in their fitness journey.

In a nation where 39.6% of adults grapple with obesity, the imperative for fitness has never been more pressing in the United States. Despite the accessibility and affordability of gyms, these alarming statistics suggest that barriers beyond mere availability hinder citizens from engaging in regular exercise. One key issue is the lack of knowledge, exacerbated by the often expensive price of personal training, averaging around $65 per hour. In response, I seek to develop an open-source app employing pose detection technology, designed to serve as a virtual personal trainer, offering insightful feedback on users’ workout routines.

Harnessing the capabilities of the open-source convolutional neural network framework, MediaPipe, my app seeks to enhance accuracy through tailored training with diverse exercise datasets, accommodating various demographic needs. The app’s calibration process involves users inputting their height, weight, and fitness goals, with subsequent comparison of their workout videos to reference videos showcasing impeccable form. Each exercise selection prompts the display of a reference video, allowing users to learn from an exemplary repetition. Real-time feedback during a workout set will guide users, correcting form and adjusting weights based on the perceived difficulty of each repetition. Prioritizing key feedback akin to a personal trainer, the app aspires to assist and enhance the fitness journeys of individuals within the community, ultimately elevating the quality and sustainability of their workouts.

After considering various options, I have chosen to use the MediaPipe framework developed by Google to extract the skeletal model from the video. I will now try to find data to train the framework into a model and successfully set up my virtual environment to run the code on my PC.

Thank you for reading! Stay tuned for next week’s updates where I will be discussing the dataset I compiled.