Physical Activity Response Determination Using Neural Network Models

The patent uses existing sensor data (heart rate, motion parameters) as copies or proxies for more complex physiological measurements. Instead of directly measuring complex responses like fatigue levels or sleep stages, the system copies information from easily measurable parameters and uses neural network models to infer the desired measurements, achieving high precision without complex measurement equipment

The patent replaces complex mechanical or physiological measurement systems with an information processing system. Instead of using complex sensors to directly measure physical activity response, the system substitutes a neural network model that processes simple sensor inputs (heart rate, motion data) to generate accurate response measurements, trading mechanical complexity for computational intelligence

The patent implements a feedback mechanism where the neural network model continuously processes sensor data and provides feedback about the user's physical activity response state. This feedback loop allows the system to maintain accurate measurements by continuously adjusting its interpretation of sensor data based on patterns learned from training, preventing information loss through intelligent data utilization

The patent creates a composite information processing system that combines multiple data sources (heart rate, motion parameters) and multiple processing techniques (neural networks, pattern recognition) into a unified measurement approach. This composite system preserves more information than individual measurement methods by integrating multiple complementary data streams