Motion Classification Using Repetitiveness to Prevent False Device Triggers
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Solution Overview
Problem
Existing systems struggle to differentiate between human and non-human motion accurately, leading to unnecessary device activation and resource waste.
Innovation Solution
A computing system processes sensor data using object-motion attributes such as repetitiveness, speed, acceleration, and path changes to classify motion as human or non-human, thereby controlling device activation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the computing system responds to all detected object motion by causing device action, then the device will be activated whenever motion is detected, but this leads to unnecessary activation when non-human objects move, wasting resources and providing poor user experience
Solution Approach 1:
The system changes parameters of sensor data processing by analyzing multiple attributes of object motion including speed, acceleration, path changes, and temporal patterns. By evaluating these parameters collectively, the system distinguishes human motion from non-human motion without requiring additional sensors, thereby reducing unnecessary device activation while maintaining high classification accuracy
Solution Approach 2:
The patent replaces physical sensor differentiation (using more sophisticated or numerous sensors to distinguish human from non-human motion) with computational analysis. The computing system substitutes mechanical/sensor-based discrimination with algorithmic classification based on motion attribute patterns, achieving the same differentiation goal with existing sensor infrastructure
2Measurement precision
If more sophisticated sensors and/or a greater number of sensors are implemented to distinguish human motion from non-human motion, then the accuracy of motion classification improves, but the cost and complexity of the system increases
Solution Approach 1:
The existing motion sensor is made multi-functional by extracting multiple attributes (speed, acceleration, path changes, temporal patterns) from a single sensor data stream. This universal utilization of the existing sensor replaces the need for multiple specialized sensors, achieving high measurement precision while maintaining low device complexity
Solution Approach 2:
Instead of using multiple physical sensors to capture different aspects of motion, the system creates virtual copies of motion information by deriving multiple attributes (speed, acceleration, path changes) from a single sensor's data. This computational copying achieves comprehensive motion analysis without the hardware complexity of multiple sensors
Data Source
AI summary
A method and system for evaluation of object motion repetitiveness as a basis to distinguish between human motion and non-human motion, in order to facilitate control of device operation. An example method includes (i) a computing system receiving sensor data representing object motion detected by at least one sensor, the object motion defining motion of an object, (ii) the computing system making a determination, based at least on an evaluation of the received sensor data, of whether the detected object motion is repetitive, and (iii) based at least on the determination being that the detected object motion is not repetitive, the computing system responding to the detected object motion by causing a device to take an action corresponding with a prediction that the object is a human being.