Visible to the public Classifying Text-Based Computer Interactions for Health Monitoring

TitleClassifying Text-Based Computer Interactions for Health Monitoring
Publication TypeJournal Article
Year of Publication2015
AuthorsVizer, L. M., Sears, A.
JournalIEEE Pervasive Computing
KeywordsAging, area under curve, AUC, classification rate, classifying text-based computer interaction, cognitive function, cognitive impairment, Computational modeling, computer interaction pattern, constructed statistical model, continuous monitoring, current assessment, Data models, Dementia, Health Care, health monitoring, healthcare, healthcare provider, human computer interaction, human-computer interaction, keystroke analysis, Monitoring, patient monitoring, pattern classification, personal health informatics, Pervasive computing, Pragmatics, Predictive models, PreMCI, premild cognitive impairment, pubcrawl170115, text analysis, unobtrusive method

Detecting early trends indicating cognitive decline can allow older adults to better manage their health, but current assessments present barriers precluding the use of such continuous monitoring by consumers. To explore the effects of cognitive status on computer interaction patterns, the authors collected typed text samples from older adults with and without pre-mild cognitive impairment (PreMCI) and constructed statistical models from keystroke and linguistic features for differentiating between the two groups. Using both feature sets, they obtained a 77.1 percent correct classification rate with 70.6 percent sensitivity, 83.3 percent specificity, and a 0.808 area under curve (AUC). These results are in line with current assessments for MC-a more advanced disease-but using an unobtrusive method. This research contributes a combination of features for text and keystroke analysis and enhances understanding of how clinicians or older adults themselves might monitor for PreMCI through patterns in typed text. It has implications for embedded systems that can enable healthcare providers and consumers to proactively and continuously monitor changes in cognitive function.

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