System and method for modulating a light-emitting peripheral device based on an unscripted feed using computer vision

The system is divided into modular components: computer vision processing unit, machine learning model, haptic command generator, and peripheral device controllers. Each module handles a specific function, allowing the complex system to be managed through independent, interchangeable units that can be developed and optimized separately while maintaining high haptic realism through precise coordination between modules.

The haptic system is designed with universal integration capabilities that allow it to work with multiple types of peripheral devices (haptic gloves, vests, chairs, motion platforms) through standardized interfaces. The computer vision processing and machine learning components serve multiple functions: tracking user movement, analyzing virtual environment events, generating haptic commands, and adapting to different content types, thereby managing complexity through multi-functional design.

The machine learning model is pre-trained offline on extensive datasets of virtual environment events and corresponding haptic responses. This preliminary training phase allows the model to learn complex patterns and relationships without requiring extensive compute resources during real-time operation. During actual haptic command generation, the pre-trained model quickly processes incoming data and generates appropriate commands with minimal latency, maintaining both adaptability and real-time performance.

The system implements continuous feedback loops where the machine learning model receives real-time data from computer vision processing and peripheral device sensors, generates haptic commands, and monitors the resulting user responses. This feedback mechanism allows the model to dynamically adapt to user preferences and environmental conditions while maintaining efficient compute throughput through iterative optimization rather than exhaustive processing.

The computer vision processing pipeline is segmented into multiple specialized stages: initial frame capture, key feature detection, event classification, and coordinate mapping. Each stage processes only the most relevant information at its level, filtering out unnecessary data before passing it to the next stage. This segmentation maintains high event tracking accuracy by preserving detailed visual information where needed while reducing overall processing load and improving speed through selective computation.

The system applies partial processing to most video frames (detecting only significant changes or events) while applying full processing only to frames containing critical haptic-triggering events. This approach maintains high accuracy for important events while reducing processing speed requirements for routine frames, effectively balancing measurement precision and processing speed through selective application of computational resources.