Keystroke Tensor Analysis for Health Condition Assessment
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Solution Overview
Problem
Current methods lack an effective way to assess health conditions such as cognitive impairment, motor control, and behavioral changes through keystroke analysis, which are unique to specific diseases like Alzheimer's and Parkinson's, with existing technologies failing to provide accurate and reliable assessments.
Innovation Solution
The system collects and analyzes keystroke data, processing augmented and enriched keystroke data to create keystroke tensors, which are then fed into machine learning algorithms to assess health conditions, recommending treatments based on the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keystroke data analysis is used to assess health conditions, then diagnostic accuracy for diseases like Alzheimer's and Parkinson's is improved, but the complexity of data processing and analysis increases
Solution Approach 1:
The system segments keystroke data into multiple dimensions including timing patterns (key press duration, interval between keys), spatial patterns (keyboard zone transitions, hand usage distribution), and typing dynamics (speed variations, error rates). This segmentation transforms complex raw data into structured features that can be independently analyzed, resolving the contradiction by making the complex data manageable while maintaining diagnostic accuracy.
Solution Approach 2:
The patent transforms one-dimensional keystroke timing data into multi-dimensional analysis by incorporating spatial keyboard zone information, hand dominance patterns, and temporal sequences. This dimensional expansion creates richer feature spaces that enhance diagnostic precision while the systematic organization of these dimensions prevents overwhelming complexity through structured processing pipelines.
2Reliability
If multiple keystroke features are analyzed to improve health assessment accuracy, then the reliability of disease detection is improved, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary processing of keystroke data by pre-calculating and storing derived features such as inter-key intervals, keyboard zone transitions, and typing speed metrics during data collection. This preliminary action prepares the data in advance, reducing the computational burden during actual analysis and enabling faster, more reliable disease detection without sacrificing assessment accuracy.
3Adaptability or versatility
If natural typing data is collected without restrictions to improve ecological validity, then the real-world applicability is improved, but the control over data quality and consistency decreases
Solution Approach 1:
The system applies different processing strategies to different segments of typing data based on their characteristics. For example, it identifies and separately handles pause periods, correction sequences, and sustained typing segments with appropriate analysis methods for each. This local quality approach maintains data consistency within each segment while preserving the natural variability across the entire typing sample, resolving the contradiction between ecological validity and data consistency.
Data Source
AI summary
Data regarding typing by a user may be collected and analyzed in order to assess one or more health conditions or functional states of the user. Each health condition that is assessed may be a disease or a symptom of a disease. For instance, based on the typing data, a computer may assess the presence, severity or probability of, or a change in, one or more symptoms such as: mild cognitive impairment; dementia; impairment of fine motor control; impairment of sensory-motor feedback; or behavioral impairment. A computer may calculate keystroke tensors that encode information about the typing. A computer may select or derive features from the keystroke tensors. These features may be fed into one or more machine learning algorithms, which in turn may output an assessment of a health condition or functional state of the user.