Wearable sEMG Biosensor Gesture Authentication
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Solution Overview
Problem
Current security measures for accessing secure devices and accounts are cumbersome and prone to breaches, requiring improvements in both security and convenience for users.
Innovation Solution
A wearable authentication device that uses surface electromyogram (sEMG) signals, in conjunction with a user device, to perform multi-factor authentication by detecting muscle gestures and generating user signatures for verification, complementing traditional authentication methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures (fingerprint recognition, password protection, facial ID) are used, then security is provided, but user convenience deteriorates and the process becomes tedious
Solution Approach 1:
The patent replaces traditional mechanical/biometric authentication systems (fingerprint scanners, facial recognition cameras) with an sEMG-based authentication system that detects electrical signals from muscle movements. This substitution enables more natural, gesture-based authentication that is both secure and convenient, resolving the contradiction between security and ease of operation.
Solution Approach 2:
The sEMG authentication system captures users' natural muscle movement signals during normal gestures and uses these signals for authentication without requiring separate authentication actions. The system serves itself by converting everyday user movements into authentication data, eliminating the need for users to perform specific authentication gestures or remember passwords.
2Reliability
If two-factor authentication is implemented, then security is boosted, but user burden increases and additional time is required
Solution Approach 1:
The patent merges multiple authentication factors into a single unified sEMG authentication process. By combining the detection of muscle movement patterns, gesture recognition, and biometric verification into one integrated system, the patent eliminates the need for separate authentication steps while maintaining high security standards, thus reducing authentication time and user burden.
Solution Approach 2:
The sEMG authentication system performs multiple authentication functions simultaneously - it detects muscle activity, recognizes gestures, verifies biometric identity, and provides security authentication all through a single sensor system. This multi-functionality consolidates what would traditionally require multiple separate authentication mechanisms into one efficient process.
3Ease of operation
If scanning sensors are used for authentication, then user identification is enabled, but security breaches become possible due to replication and lack of encryption
Solution Approach 1:
The patent captures the electrical signal patterns (sEMG) generated by users' muscle movements and creates unique digital representations of these patterns. These copied signal patterns serve as authentication credentials that are difficult to replicate, providing a secure copy-based authentication mechanism that maintains ease of operation while improving security.
Solution Approach 2:
The system transitions from using static authentication parameters (fixed passwords, unchanging fingerprint templates) to dynamic parameters (varying sEMG signal patterns that change with each muscle contraction). This parameter change enables more secure authentication that adapts to natural variations in user physiology while maintaining operational convenience.
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 security and convenience by allowing secure access to devices and accounts with improved accuracy and adaptability to evolving user biometrics, reducing the burden on users and mitigating security risks.
Implementation Method 1
The first biosensor may be configured to detect at least a first surface electromyogram (sEMG) signal on the user's skin proximate a first muscle, and occurring in response to a first movement of the first muscle to perform a first gesture
Data Source
AI summary
Before or after a first-type authentication has been completed, disclosed devices, systems, and methods may conduct a second-type authentication to authenticate a user such that the user can log into a secure device and/or access secure content. An example system may cause a wearable device to activate a biosensor, which extends along a full internal circumference of the wearable device when worn, to detect at least a first sEMG signal on the user's skin responsive to the user performing a first gesture. The system may also generate or receive a first user signature based on the first sEMG signal and determine whether the first user signature matches stored authentication training data. In response to determining that there is a match, the system may complete the second-type authentication to authenticate the user.