Keystroke Dynamics for Passive Fine Motor Monitoring
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Solution Overview
Problem
Current methods for diagnosing and monitoring neuromuscular disorders, such as electromyography (EMG), are invasive, painful, and impractical for patients with implants or hemophilia, and lack the ability to provide continuous or proactive assessment of motor function changes.
Innovation Solution
A system and method that assesses fine motor functions using keystroke dynamics and gesture biometrics, collecting raw data from user interactions to compute biometric attributes, generate session patterns, and apply a decision model to produce a scale index indicating the current motor functionality, enabling remote monitoring and daily assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EMG is used to diagnose neuromuscular disorders, then diagnostic accuracy is improved, but patient comfort and safety deteriorate due to invasive needle insertion
Solution Approach 1:
The patent replaces the mechanical needle insertion method of EMG with a computational approach using keystroke dynamics analysis. The system captures typing patterns through software and analyzes biometric attributes (key press duration, inter-key interval, error rate) to assess fine motor function, eliminating the need for physical needle insertion while maintaining diagnostic capability
Solution Approach 2:
The patent introduces keystroke dynamics as an intermediary measurement method. Instead of directly measuring muscle electrical activity through needle insertion, the system uses typing behavior as an indirect indicator of fine motor function, neuromuscular coordination, and disease progression, thereby avoiding direct tissue penetration
2Measurement precision
If standardized tests like 9-Hole Peg Test are administered, then fine motor function assessment is improved, but continuous monitoring capability deteriorates due to periodic clinic visits requirement
Solution Approach 1:
The patent enables continuous assessment by having patients perform typing tasks in their daily routine using their personal computers. The system continuously collects keystroke data over time, allowing for ongoing monitoring of fine motor function and disease progression without requiring periodic trips to the clinic, thus transforming discontinuous periodic testing into continuous home-based monitoring
Solution Approach 2:
The system allows patients to self-administer the assessment by performing typing tasks on their own computers at home. The software automatically captures keystroke dynamics data and computes biometric attributes without requiring clinician presence or specialized testing equipment, enabling patients to independently contribute to their own monitoring
3Measurement precision
If complex and prolonged tests are administered, then comprehensive evaluation is improved, but patient convenience and accessibility deteriorate
Solution Approach 1:
The patent uses the patient's existing personal computer and typing activity as the assessment medium, rather than requiring specialized testing equipment or dedicated testing sessions. The typing task itself is a natural, brief activity that patients already perform regularly, transforming a potentially burdensome test into an incidental byproduct of normal computer usage
Solution Approach 2:
The system leverages the universal activity of computer typing, which patients already perform for work, communication, and information gathering. By embedding the assessment within this existing behavior, the system eliminates the need for separate testing appointments and makes comprehensive evaluation accessible to patients in their natural environment
Data Source
AI summary
A system and method for assessing fine motor functions based on user input patterns. The method comprises collecting raw data related to at least an interaction of a user with a computing device; computing, based on the raw data, at least one biometric attribute; generating a session pattern based on the at least one biometric attribute; and generating using the session pattern and a decision model a scale index indicating a current condition of the fine motor functionality of the user data.