Mobile Device Identification via Real-Time Movement Comparison
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Solution Overview
Problem
Existing methods for identifying mobile devices rely on predetermined motion signatures, limiting flexibility and preventing the use of arbitrary or user-defined gestures for device recognition.
Innovation Solution
A method that derives and compares movement information between mobile devices, allowing for the identification of devices with matching movements without the need for pre-stored patterns, using sensors like acceleration and optical sensors to communicate and compare movement data in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predetermined motion signatures are stored in a database for gesture recognition, then the system can reliably identify predefined gestures, but the system loses flexibility and cannot recognize arbitrary or user-defined gestures
Solution Approach 1:
The system transitions from static predetermined gesture signatures to dynamic real-time movement comparison. Instead of matching against fixed stored patterns, the system dynamically compares movement information between devices as they move, allowing gestures to be defined by relative motion rather than predetermined sequences. This enables flexible user-defined gestures while maintaining reliable identification through real-time matching.
Solution Approach 2:
The system allows devices to define their own gestures through relative movement rather than requiring external pre-definition. Each device's movement is compared against another device's movement, and the matching movement itself defines the gesture. This self-service approach eliminates the need for external gesture databases while maintaining reliable gesture recognition through mutual movement verification.
2Reliability
If users must learn predetermined gestures and their meanings before use, then the system ensures consistent gesture interpretation, but the ease of operation decreases due to the learning requirement
Solution Approach 1:
The system eliminates the need for users to learn predetermined gestures by allowing gestures to be defined through relative movement between devices. When devices move in matching patterns, that movement itself becomes the gesture definition. This self-service approach maintains consistent interpretation through real-time mutual verification while dramatically improving ease of operation by eliminating the learning curve.
3Reliability
If all allowable gestures must be previously stored in a database, then the system maintains control over valid gestures, but the device complexity increases due to storage and comparison requirements
Solution Approach 1:
The system extracts the gesture validation function from centralized databases and distributes it to the devices themselves. Instead of storing all allowable gestures centrally and comparing against them, each device independently compares its movement information with another device's movement information in real-time. This extraction eliminates the need for large gesture databases and complex centralized validation systems while maintaining reliable gesture control.
Data Source
AI summary
A method of identifying mobile devices (1,1′) capable of communicating (C) with each other comprises the steps of deriving, for each mobile device, movement information from movements (M) made by the mobile device within a respective time period, and communicating the movement information. The method comprises the further steps of comparing the movement information of the mobile device (1) with the movement information of at least one other mobile device (T), and identifying mobile devices having matching movement information. The step of deriving movement information is carried out using sensors mounted in the mobile device, such as acceleration sensors and/or optical sensors. The step of comparing the movement information may be carried in a mobile device (1) or in a central unit.