Object Motion Classification for Human-Triggered Device Control

The patent changes the parameters of motion detection by analyzing multiple attributes of motion (speed, acceleration, path changes, repetitiveness) rather than relying solely on additional sensors. This allows the system to distinguish between human and non-human motion using existing sensor data processed through enhanced algorithms that evaluate temporal and spatial motion characteristics.

The patent replaces the mechanical approach of adding more physical sensors with an information-processing approach. Instead of expanding the hardware sensor array, the system uses computational analysis of motion attributes (velocity, acceleration patterns, path geometry) to achieve better classification accuracy, substituting mechanical complexity with algorithmic complexity.

The patent applies preliminary action by analyzing motion attributes before triggering device activation. The system pre-evaluates detected motion against learned patterns and motion attributes (speed thresholds, acceleration characteristics, path repetitiveness) to predict whether the motion is human before actually activating the device, preventing unnecessary activation from non-human motion.

The system uses feedback from analyzing motion patterns and comparing them against stored learned patterns to determine appropriate device activation. By continuously monitoring motion attributes and comparing them against historical data and threshold criteria, the system adjusts its activation decisions to reduce unnecessary energy consumption while maintaining responsiveness to genuine human presence.