Hand Usage Detection via Keyflight Timing Analysis
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Solution Overview
Problem
Current technologies lack the ability to determine whether a user is typing with one hand or two hands, which is essential for optimizing user experience and enhancing behavioral biometrics, as existing methods do not consider typing style to adapt keyboard layouts or positions based on user interaction.
Innovation Solution
The method involves analyzing keyflight statistics between different keys or groups of keys with distinct properties to differentiate between one-handed and two-handed typing by calculating intragroup and intergroup keyflight timing distributions, allowing for the adaptation of keyboard layouts to better suit the user's input method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyboard layout is fixed for all users, then device complexity is reduced, but user experience and typing efficiency deteriorate for users with different hand usage patterns
Solution Approach 1:
The keyboard layout transitions from a static configuration to a dynamic one that automatically adapts based on detected user hand usage patterns. The system monitors keystroke patterns and dynamically repositions virtual keys or adjusts layout configurations to match whether the user types with one hand or two hands, optimizing accessibility and typing efficiency without requiring manual user intervention.
Solution Approach 2:
The system changes the spatial parameters of the keyboard layout (key positions, clusters, and arrangements) based on detected hand usage patterns. By analyzing keystroke timing and pattern data, the system modifies layout parameters such as key distance, cluster distribution, and positioning to better suit one-handed or two-handed typing styles, thereby improving ease of operation.
2Reliability
If behavioral biometrics analysis is enhanced with hand usage detection, then authentication reliability improves, but system complexity increases
Solution Approach 1:
The behavioral biometrics system is segmented into distinct functional modules: a hand usage detection module that analyzes keystroke patterns to determine hand usage, and an authentication module that uses this information alongside other behavioral metrics. This segmentation allows the system to incorporate hand usage detection without overwhelming complexity, as each module handles specific tasks independently and contributes to the overall authentication reliability.
3Measurement precision
If keyflight timing analysis is performed to detect hand usage, then typing pattern recognition accuracy improves, but processing time increases
Solution Approach 1:
The system performs partial analysis of keyflight timing data by focusing on specific metrics that are most indicative of hand usage patterns, such as the distribution of keystroke intervals and the timing relationships between adjacent keys. Rather than analyzing every possible temporal aspect of typing, the system selectively measures the most relevant parameters, achieving sufficient recognition accuracy while minimizing processing time and computational overhead.
Data Source
AI summary
Systems and methods are provided for detecting when a user uses one or both hands to interact with a device having a physical or virtual keyboard. In one implementation, one-handed typing is determined by analyzing intragroup and intergroup keyflight timing distributions between different groups of clustered keys, each cluster having distinctly different proximal properties. A statistical measure made between the intragroup and intergroup keyflight timing distributions may be performed to determine one-handed or two-handed typing input, which may be used to enhance a user experience by adapting a viewing or input element to an appropriate hand setting. In another implementation, the detection may be used as input to determine a likelihood of suspected coaching fraud for online banking applications. In yet another implementation, the detection of one-handed typing is used as an input to bot detection algorithms to reduce false positives from other modalities.