KineticMotionAI was inspired by the idea that elite athletes have access to biomechanical analysis and expert coaching, while most people lack guidance to train safely and effectively. It analyzes sports movements using AI, detecting joint positions, evaluating form, identifying inefficiencies, predicting injury risk, and giving real-time corrective feedback along with performance scores. We built it using pose estimation models, machine learning algorithms for movement scoring, Python for backend AI processing, and a React/Flutter frontend for an intuitive, gamified dashboard, with Firebase and Google Cloud providing secure storage and authentication. The biggest challenges we faced were optimizing AI processing speed, maintaining accuracy across different lighting and camera angles, and designing a system that feels powerful yet simple for users. We’re proud of creating a scalable platform that delivers personalized feedback, gamified progress tracking, recovery insights, and a daily-use experience that motivates consistent improvement. Through this process, we learned how to integrate AI with human-centered design, balance speed and precision, and translate complex motion data into actionable guidance. Looking forward, we plan to expand sports coverage, improve real-time AI responsiveness, integrate wearable data, add predictive analytics, and continue refining KineticMotionAI into a full daily performance companion.
Built With
- analysis
- and
- biomechanical
- custom
- firebase-(authentication
- firestore
- google-cloud-platform-(cloud-functions
- hosting)
- javascript
- mediapipe-(pose-estimation)
- opencv
- python
- pytorch
- react-(or-flutter-for-mobile)
- rest-apis
- storage)
- tensorflow
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