Sensorimotor Control Loop Model for User Identity Detection
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Solution Overview
Problem
Current security measures for electronic devices lack effective methods to reliably authenticate users and detect fraudulent activities, particularly in scenarios where traditional username and password combinations are insufficient, such as in online banking or remote access applications.
Innovation Solution
The system employs a sensorimotor control loop model to monitor and analyze pointing device dynamics and gestures, estimating user-specific parameters that characterize motor-control traits, allowing for continuous user authentication and fraud detection by distinguishing between genuine and fraudulent users based on unique motor-control patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional username and password authentication is used, then user authentication can be performed, but security is insufficient to reliably detect fraudulent activities
Solution Approach 1:
The patent replaces traditional mechanical authentication systems (username/password entry) with a sensorimotor control loop model that analyzes unconscious motor behaviors. This substitution captures physiological motor control patterns through pointing device dynamics, providing more reliable fraud detection since these patterns are difficult to replicate consciously, while avoiding the complexity of multiple authentication factors.
Solution Approach 2:
The patent introduces an intermediary layer between authentication verification and user interaction. The sensorimotor control loop model acts as a mediator that continuously analyzes pointing device dynamics and motor behaviors without disrupting the user experience. This intermediary provides enhanced security through unconscious behavior analysis while maintaining simple user interaction.
2Reliability
If sensorimotor control loop model is implemented, then user authentication reliability is improved, but system complexity increases
Solution Approach 1:
The sensorimotor control loop model performs self-service by automatically capturing and analyzing motor control patterns without requiring explicit user participation. The system continuously monitors pointing device dynamics and gestures, extracting authentication features from unconscious behaviors naturally exhibited during task performance, eliminating the need for separate authentication actions from the user.
Solution Approach 2:
The patent implements preliminary action by establishing the sensorimotor control loop model during initial system setup or first use. The model is trained on user-specific motor control patterns and stored for future authentication comparisons. This preliminary characterization enables rapid, reliable fraud detection in subsequent sessions without requiring complex real-time analysis infrastructure.
3Measurement precision
If continuous monitoring of pointing device dynamics is performed, then authentication accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively monitoring specific pointing device dynamics and gestures that are most discriminative for authentication. Rather than analyzing all possible motor behaviors continuously, the system focuses on key sensorimotor parameters such as pointing accuracy, movement smoothness, and gesture patterns. This selective monitoring maintains high detection precision while reducing computational overhead and processing time.
Solution Approach 2:
The system performs preliminary characterization of user motor control patterns during initial sessions, storing these profiles for rapid comparison during authentication. This pre-computed baseline enables fast fraud detection by comparing current sensorimotor measurements against stored profiles using efficient similarity metrics, minimizing real-time processing requirements while maintaining high measurement precision.
4Reliability
If user-specific parameters are stored in database, then fraud detection capability is improved, but data storage requirements increase
Solution Approach 1:
The patent extracts only the essential sensorimotor control parameters needed for authentication from the full set of possible motor behaviors. The system identifies and stores key features such as pointing device dynamics characteristics, gesture patterns, and motor control loop parameters that uniquely identify users. This extraction of critical authentication features reduces data storage requirements while maintaining high user identification reliability.
Solution Approach 2:
The system transforms raw sensorimotor measurements into compressed parameter representations suitable for storage and comparison. By converting continuous motor control data into discrete authentication features and parameters, the patent reduces the quantity of stored data while preserving the essential information needed for reliable fraud detection. Parameter transformation enables efficient storage and rapid comparison operations.
Data Source
AI summary
Device, system, and method of detecting identity of a user based on motor-control loop model. A method includes: during a first session of a user who utilizes a pointing device for interacting with a computerized service, monitoring the pointing device dynamics and gestures of the user; based on the monitored dynamics and gestures, estimating parameters that characterize a sensorimotor control loop model of the user; storing in a database a record indicating that the user is associated with the parameters that characterize the sensorimotor control loop model of the user.