Neuro-mechanical Fingerprint Authentication via Micro-motion Signals
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Solution Overview
Problem
Existing user authentication methods, relying on anatomical and behavioral biometrics, are not foolproof, prone to data theft, costly, energy-intensive, and invasive, while also requiring centralized databases and increased hardware, compromising user privacy and security.
Innovation Solution
The use of 3-D sensors and signal processing to capture 'neuro-mechanical fingerprints' (NFPs) from micro-motion signals representing unique neuro-muscular functions, which are translated into well-defined micro-motions, providing a decentralized, low-power, and privacy-respecting authentication solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anatomical and behavioral biometrics are used for authentication, then user identification accuracy is improved, but data security and privacy are worsened due to data theft risks and centralized database requirements
Solution Approach 1:
The patent extracts and processes biometric data locally on the user's device rather than transmitting it to centralized databases. The neuro-mechanical fingerprint is generated and verified locally, eliminating the security vulnerability of centralized data storage while maintaining identification accuracy.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw biometric signals into encrypted neuro-mechanical fingerprints. This intermediary step ensures that even if data is intercepted, the original biometric information cannot be reconstructed, thus protecting user privacy while maintaining authentication reliability.
2Reliability
If additional hardware is added to capture biometrics, then authentication capability is improved, but device cost and complexity are worsened
Solution Approach 1:
The patent makes existing multi-functional sensors (accelerometers, gyroscopes, touchscreens) perform the additional function of capturing neuro-mechanical biometric data. This eliminates the need for dedicated biometric hardware while maintaining authentication capability, thus reducing device complexity and cost.
Solution Approach 2:
The patent enables the device's existing sensors to serve dual purposes: their original functions plus biometric authentication. The system uses the device's own motion and touch data to generate authentication credentials, eliminating the need for external specialized hardware.
3Measurement precision
If behavioral aspects are used for authentication, then user identification is improved, but energy consumption and system power requirements are worsened
Solution Approach 1:
The patent continuously captures motion and touch data during normal device usage without requiring separate authentication actions. The neuro-mechanical fingerprint is generated from ongoing sensor data, eliminating the need for additional power-consuming authentication interactions while maintaining continuous identification capability.
Solution Approach 2:
The system uses data already being collected by the device's sensors for other purposes (motion tracking, touch input) and repurposes it for authentication. This eliminates redundant data collection and processing, significantly reducing energy consumption while maintaining identification accuracy.
4Reliability
If centralized databases are used to store user data, then authentication verification is improved, but user privacy and data protection are worsened
Solution Approach 1:
The patent extracts the authentication verification function from centralized databases and relocates it to local device processing. The neuro-mechanical fingerprint is verified locally without transmitting sensitive biometric data to remote servers, thus maintaining verification reliability while protecting user privacy.
Solution Approach 2:
The patent creates a local copy of the authentication verification capability on each user's device. Instead of relying on a centralized database, each device independently verifies neuro-mechanical fingerprints, eliminating the need to store or transmit sensitive user data while maintaining authentication reliability.
Data Source
AI summary
In accordance with one embodiment, an access control system is disclosed. The access control system comprises an access control panel including a touchable surface, a multi-dimensional touch sensor under the touchable surface, and a processor coupled to the multi-dimensional touch sensor. The multi-dimensional touch sensor captures a multi-dimensional motion signal including a micro-motion signal component representing neuro-mechanical micro-motions of a user touching the multi-dimensional touch sensor. The processor performs signal processing of the multi-dimensional motion signal to obtain the micro-motion signal component; and extracts unique values of predetermined features from the micro-motion signal component to form a neuro-fingerprint (NFP) that uniquely identifies the user. The NFP can be used as a gatekeeper to control entry into homes, offices, buildings, or other real properly typically protected by access control.