Classifying Text-Based Computer Interactions for Health Monitoring

Area of Science:

• Gerontology
• Neurology
• Computational Linguistics

Background:

• Cognitive decline in older adults poses a significant public health challenge.
• Early detection of cognitive impairment is crucial for effective management and intervention.
• Current diagnostic methods can be invasive or require specialized settings.

Purpose of the Study:

• To introduce a novel, non-invasive assessment approach for early cognitive decline detection.
• To leverage ordinary text-typing activities as a tool for cognitive monitoring.
• To explore the potential of statistical modeling of typing features for identifying early signs of cognitive impairment.

Main Methods:

• Development of a statistical model analyzing keystroke dynamics and linguistic features during typing.
• Utilizing data from everyday text-typing activities of older adults.
• Correlation analysis between typing patterns and indicators of cognitive status.

Main Results:

• The proposed assessment approach successfully identifies subtle changes in typing patterns associated with early cognitive decline.
• Statistical models demonstrated significant correlations between specific keystroke and linguistic features and cognitive status.
• The method offers a feasible way to monitor cognitive health through routine digital interactions.

Conclusions:

• Typing behavior analysis presents a promising, accessible method for early cognitive decline screening in older adults.
• This approach can facilitate widespread, continuous monitoring of cognitive health.
• Further research can refine the model for improved diagnostic accuracy and clinical integration.