Smart Dance Shoes with Machine Learning Powered Light and Motion Synchronization

Abstract

In the era of wearable technology, integrating machine learning into performance arts opens new dimensions for user interaction and creativity. This project presents the development of Smart Dance Shoes that utilize motion sensors and machine learning algorithms to deliver real-time RGB light synchronization based on dance movements. The system is built using the ESP32-S3 microcontroller and the MPU-6050 sensor, which capture accelerometer and gyroscope data from the dancer’s movements. These data inputs are processed through a machine learning model developed on Edge Impulse, which classifies different dance gestures such as jumps, spins, and steps and triggers corresponding lighting effects to enhance visual performance. The hardware is designed to be lightweight, portable, and user-friendly, making it suitable for dancers, performers, and fitness enthusiasts. Key components include RGB LED neon strips, a 3.7V LiPo battery, and Bluetooth integration for wireless customization. Testing covered unit, integration, and performance evaluations to ensure stability, low latency, and energy efficiency. Future improvements include multi-shoe synchronization, music-responsive lighting, and advanced models such as LSTM. This project demonstrates the potential of intelligent wearables in enhancing interactive and immersive experiences in performing arts.
Keywords: Wearable technology, Machine learning, Motion recognition, RGB lighting, Smart dance