Mobile Device Location Classification Using Accelerometer Data
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Solution Overview
Problem
Traditional passive entry and passive start (PEPS) systems for vehicles face challenges in accurately determining the location of a mobile network device on a person due to signal interference and absorption by the human body, leading to inconsistent distance estimation and operational decisions.
Innovation Solution
A system that includes a data module, classification module, and control module to receive acceleration and gravity data from a mobile network device, classify its location on a person, and perform vehicle operations based on the classification, using a neural network to process data and determine the most likely location of the device, thereby improving accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If signal transmission through the human body is used for location detection, then passive entry and start functions can be implemented, but signal interference and absorption by the human body cause inconsistent distance estimation and reduce measurement precision
Solution Approach 1:
The patent segments the location determination process into two independent components: (1) distance estimation using signal strength, and (2) location classification using accelerometer data. This segmentation allows each component to operate optimally without interference from the other, resolving the contradiction by maintaining passive operation while improving location precision through alternative measurement methods.
Solution Approach 2:
The patent introduces an intermediary system consisting of accelerometer sensors and a classification algorithm that acts as a mediator between the signal transmission system and the location determination system. This intermediary provides additional location context that compensates for signal interference and absorption, thereby improving measurement precision without affecting the passive operation capability.
2Productivity
If signal strength-based distance estimation is used, then passive entry and start operations can be performed, but signal absorption by the human body leads to unreliable distance measurements
Solution Approach 1:
The patent divides the location determination system into two independent modules: a distance estimation module using signal strength for quick response, and a location classification module using accelerometer data for reliable position identification. This segmentation allows the system to maintain fast operation execution while improving measurement reliability through the complementary accelerometer-based classification.
Solution Approach 2:
The patent implements a feedback mechanism where the classification module continuously monitors accelerometer data and provides correction feedback to the distance estimation process. When signal absorption causes unreliable distance measurements, the feedback from accelerometer-based location classification compensates for these errors, thereby maintaining both operational speed and measurement reliability.
3Device complexity
If only signal transmission data is used for location determination, then the system remains simple, but the system cannot accurately distinguish device placement locations on the person
Solution Approach 1:
The patent merges two different measurement systems: signal transmission data for distance estimation and accelerometer data for location classification. By combining these complementary data sources, the system achieves accurate location classification without requiring a complete redesign of the system architecture, thus balancing device complexity with measurement precision.
Solution Approach 2:
The patent makes the mobile network device multi-functional by utilizing both its signal transmission capability (for communication and distance estimation) and its accelerometer capability (for motion detection and location classification). This multi-functionality allows the system to improve location classification accuracy without adding separate dedicated hardware, thereby managing device complexity effectively.
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
Enhances the accuracy of location classification, allowing for more precise operational decisions in vehicle systems, such as unlocking doors or starting the engine, by accounting for rotational changes and minimal acceleration, thus improving user experience and system reliability.
Implementation Method 1
receive at least one of acceleration data and gravity data from an accelerometer or a mobile network device, where the acceleration data and the gravity data are indicative of accelerations experienced by the mobile network device
Data Source
AI summary
A system is provided including a data module, a classification module and a control module. The data module is configured to receive at least one of acceleration data and gravity data from an accelerometer or a mobile network device, where the acceleration data and the gravity data are indicative of accelerations experienced by the mobile network device. The classification module is configured to classify a location of the mobile network device on a person based on the at least one of the acceleration data and the gravity data and generate a location classification output. The control module is configured to perform an operation based on the location classification output.