Motion Sensor Pattern Analysis for GPS Boundary Monitoring
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Solution Overview
Problem
GPS-based asset tracking systems fail to detect and alert users to non-positional movements or unusual movements within a monitored area, missing valuable information that could indicate safety threats, such as distress signals from dependents like children or pets.
Innovation Solution
A system that uses motion sensors like accelerometers or gyroscopes to analyze movement patterns, comparing them to learned patterns, and notifies users or performs actions when abnormal or reportable movements are detected, with boundaries defined by user input or other monitoring systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS-based asset tracking systems monitor only location data within a defined area, then the system complexity remains low and ease of operation is maintained, but the system fails to detect non-positional movements or unusual movements that could indicate safety threats
Solution Approach 1:
The patent combines GPS location tracking with motion sensing technology into a single integrated system. The motion sensor detects non-positional movements while the GPS component tracks location, and both data streams are processed together by a single processor to determine whether alerts should be generated. This merging allows the system to detect both positional boundary violations and non-positional unusual movements without requiring entirely separate monitoring systems.
Solution Approach 2:
The patent creates a multi-functional monitoring system that can detect multiple types of events: boundary violations (positional), unusual movements (non-positional), and patterns of behavior. The same processor and alerting infrastructure handle all these different monitoring functions, making the system versatile rather than requiring separate specialized systems for each type of detection.
2Measurement precision
If motion sensors and pattern analysis are added to GPS tracking systems, then detection of unusual movements and safety threats is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary action by pre-defining geographic boundaries and motion thresholds before monitoring begins. The processor is pre-programmed with the geographic boundary coordinates and motion sensor threshold values, allowing it to immediately compare incoming data against these predetermined criteria without requiring complex real-time analysis or adaptive learning algorithms.
Solution Approach 2:
The patent uses parameter changes by establishing predetermined threshold values for motion sensor readings and geographic boundary coordinates. The system monitors whether motion sensor data exceeds these threshold parameters or whether GPS coordinates fall outside the defined boundary parameters, simplifying the detection logic to straightforward parameter comparisons rather than complex pattern recognition.
3Loss of information
If the system monitors only positional data relative to physical area boundaries, then data processing requirements remain low, but valuable information about abnormal activities within the monitored area goes unreported
Solution Approach 1:
The patent segments the monitoring function into two independent components: positional monitoring (GPS coordinates against boundary definitions) and non-positional monitoring (motion sensor readings against motion thresholds). This segmentation allows each component to process its specific data type independently using simple comparison logic, avoiding the need for complex integrated analysis while still capturing both types of information for comprehensive safety monitoring.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the detection of critical movement patterns within a GPS-defined area, providing timely alerts and control actions, enhancing user awareness of safety and activity monitoring beyond traditional location-based tracking.
Implementation Method 1
Data is acquired from a motion sensor, such as a micro-electro-mechanical systems (MEMS) sensor like an accelerometer or a gyroscope
Implementation Method 2
Data is acquired from a motion sensor, such as a micro-electro-mechanical systems (MEMS) sensor like an accelerometer or a gyroscope
Data Source
AI summary
A method for monitoring motion of an entity within a predetermined boundary established using a location detection technology. Sensor data is acquired from a motion sensor that senses non-positional movement of the entity and is attachable to the entity. A learned movement pattern associated with the entity is accessed. Computing techniques are used to analyze the acquired sensor data in relationship to the learned movement pattern. A current movement pattern is identified based on the analysis. It is determined whether the current movement pattern is a reportable movement pattern, and if so, a predetermined action is performed.