Future Scope

Potential Improvements

• AI-based gesture recognition: Integrate computer-vision pipelines with deep learning models (e.g., CNN/LSTM hybrids) to enable real-time gesture classification, reducing dependency on manual calibration and improving robustness across varying lighting and user profiles.
• Dynamic difficulty adjustment using ML: Implement reinforcement learning or supervised player modeling to adapt gameplay parameters (speed, complexity, response thresholds) based on user performance metrics and engagement signals.
• Multi-player mode: Extend the input layer to handle concurrent user tracking with collision resolution and session management, enabling synchronous multiplayer experiences with shared projection space.
• Mobile app integration: Develop a companion mobile client for remote control, configuration, telemetry visualization, and user authentication via RESTful APIs or WebSocket-based communication.
• Enhanced 3D projection mapping: Introduce spatial mapping and surface reconstruction to support non-planar projection, enabling accurate texture warping and depth-aware rendering on complex geometries.

Scalability

The system can be extended to: - Educational AR games - Interactive installations - Smart classroom environments