From Concept to Reality: The Journey of Building a Mobile CV-Based Human Pose Detection App
Tags: computervision, tensorflow, ios, android
Published: 2024-03-20 17:58:44

In the dynamic landscape of tech innovation, the story of creating something truly groundbreaking often starts with a spark—an idea that challenges the status quo. My journey through developing a mobile CV-based Human Pose Detection app, designed to revolutionize exercise detection, is a tale of technology, teamwork, and transformation. As the CTO of a vibrant team of five developers, I led the architectural design and DevOps orchestration of a project that initially flirted with IoT concepts before embracing the transformative power of Computer Vision (CV) technology.


![FITFI Promo post](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1k3h0tnz0bey2fyt5kv2.jpeg)

## The Genesis: IoT and 9 DOF Sensors

Our adventure began in 2019, with the ambition to merge the physical and digital worlds through the lens of IoT. We aimed to create a solution that could accurately detect and analyze human poses, catering especially to fitness enthusiasts who sought precision in their exercise routines. The prototype phase saw us designing and creating five iterations of a custom board, each embedded with a 9 Degree of Freedom (9 DOF) sensor. This sensor was pivotal in capturing motion data, a task that required us to dive deep into the realms of firmware development, ensuring our hardware could seamlessly communicate with the software we were envisioning.

## Team Synergy: A Blend of Expertise

The journey was not a solo endeavor; it was fueled by the collective expertise of a five-developer team, with roles spanning from data scientists to backend specialists. My role as CTO was to architect the application's backbone and spearhead our DevOps strategy, ensuring that our development lifecycle was as smooth and efficient as the exercises we aimed to track. Our backend, a robust infrastructure deployed on AWS Cloud utilizing Elastic Beanstalk and Serverless technologies, was the digital scaffolding that supported our ambitious project.

##  Shift in Paradigm: Embracing Computer Vision


![FITFI Screenshot](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ervdxxjpval6mvbg46pf.jpeg)

As our project evolved, so did our understanding of the best tools to achieve our goal. The initial IoT-centric approach, while innovative, led us to a pivotal realization—the future of human pose detection in our project lay in the power of Computer Vision. This epiphany prompted a strategic pivot to CV technology, leveraging TensorFlow Lite and TensorFlow Pose Estimation SDKs. This transition was not merely a change in technology but a leap towards creating a more scalable, efficient, and user-friendly solution.

## The Local Processing Revolution

One of the groundbreaking aspects of our app was the ability to process sensor data locally on mobile devices. Our data scientist played a crucial role in this, receiving sensor data via Bluetooth and crunching numbers on the fly. This local processing not only enhanced performance but also laid the groundwork for our model to learn and improve over time. I took charge of creating a pipeline that facilitated this continuous improvement cycle, ensuring that our app became smarter and more accurate with each exercise it analyzed.

## On-Device Magic: TensorFlow Lite and Pose Estimation

The decision to incorporate TensorFlow Lite and TensorFlow Pose Estimation SDKs marked a significant milestone in our app's development. By running CV algorithms directly on the device, we unlocked real-time pose detection capabilities, enabling users to receive instant feedback on their exercise form. This on-device processing was not just a technical feat; it was a user experience revolution, ensuring that our app could operate seamlessly, even in the most bandwidth-constrained environments.

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## Lessons Learned and Paths Forward

The journey from an IoT-based concept to a CV-powered reality taught us invaluable lessons about innovation, flexibility, and the importance of staying ahead of the technological curve. As a CTO, guiding a team through this transformative process was both a challenge and a privilege. It reaffirmed my belief in the power of collaboration, the importance of a solid architectural foundation, and the need for a forward-thinking DevOps strategy.

Creating the mobile CV-based Human Pose Detection app was a journey of technological evolution, driven by a vision to blend physical activity with digital precision. It was a testament to the power of innovation, the value of adaptable strategies, and the incredible potential of combining IoT with Computer Vision to create solutions that impact lives. As we look to the future, the lessons learned and the successes achieved fuel our passion for pushing the boundaries of what's possible, one pose at a time.