Motion Play

ESP32-based sensor system for detecting and tracking fast-moving objects through a detection plane. Designed for games and sports performance training.

Project Status

Phase 4 - Integration & Polish (77% complete)

• ✅ Custom PCBs with dual-MUX sensor architecture
• ✅ 1000 Hz data collection with PSRAM buffering
• ✅ AWS IoT Core + Lambda + DynamoDB backend
• ✅ React web interface with visualization and device control
• 🔄 Testing, optimization, documentation

Repository Structure

motion-play/
├── .context/           # AI context documentation (start here)
│   ├── PROJECT.md      # Comprehensive project context
│   ├── manifest.yaml   # Structured metadata
│   └── IDEAS.md        # Feature backlog
├── firmware/           # ESP32-S3 firmware (PlatformIO/Arduino)
├── frontend/           # React web interface
├── lambda/             # AWS Lambda functions
├── infrastructure/     # AWS setup and deployment
├── hardware/           # KiCad PCB designs
│   ├── pcb-main/       # Main controller board
│   ├── sensor-rigid/   # Sensor rigid base (mux, connectors)
│   └── sensor-flex/    # Sensor flex strip (VCNL4040s)
└── docs/references/    # Hardware datasheets

Quick Start

# Firmware
cd firmware && pio run -t upload

# Frontend
cd frontend/motion-play-ui && npm install && npm run dev

Documentation

For AI assistants and detailed context: See .context/PROJECT.md

Hardware reference materials: See docs/references/

Hardware

• MCU: LilyGO T-Display-S3 (ESP32-S3)
• Sensors: 6× VCNL4040 proximity sensors (3 assemblies × 2 sensors)
• Sensor Design: Rigid+Flex hybrid PCBs (sensors on flex strip angle outward for better direction detection)
• Architecture: TCA9548A → PCA9546A → VCNL4040 (dual I2C multiplexing)

License

[TBD]