Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach

Abstract

Cognitive impairment has a significantly negative impact on global healthcare and the community. Holding a person’s cognition and mental retention among older adults is improbable with aging. Early detection of cognitive impairment will decline the most significant impact of extended disease to permanent mental damage. This paper aims to develop a machine learning model to detect and differentiate cognitive impairment categories like severe, moderate, mild, and normal by analyzing neurophysical and physical data. Keystroke and smartwatch have been used to extract individuals’ neurophysical and physical data, respectively. An advanced ensemble learning algorithm named Gradient Boosting Machine (GBM) is proposed to classify the cognitive severity level (absence, mild, moderate, and severe) based on the Standardised Mini-Mental State Examination (SMMSE) questionnaire scores. The statistical method “Pearson’s correlation” and the wrapper feature selection technique have been used to analyze and select the best features. Then, we have conducted our proposed algorithm GBM on those features. And the result has shown an accuracy of more than 94%. This paper has added a new dimension to the state-of-the-art to predict cognitive impairment by implementing neurophysical data and physical data together.

A scoping review of different monitoring-technology devices in caring for older adults with cognitive impairment

“…A total of 21 articles were included in this study and were published between the years 2008 and 2022 (Table 1). The majority of the included studies were conducted in the United States (12,(26)(27)(28)30) (n = 5), followed by China (n = 5) (10,16,27,34,37), Italy (n = 3) (14,29,33), and South Korea (n = 2) (17,34). One article each was conducted in Greece (n = 1) (13); the United Kingdom (11); Japan (15); Thailand (32); Sweden (6); and Australia (36).…”

“…Monitoring-technology devices are particularly important for older adults to assess and detect both their cognitive and functional statuses, as they can help them to evaluate their overall health condition, maintain their independence, and improve their quality of life (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). Currently, several technology devices are being developed to monitor the health problems of older adults, including CI (6)(7)(8)(9).…”

“…The use of the near-infrared spectroscopy (NIRS) system as an alternative imaging system is also useful in analyzing brain activation in cognitive disorders (16). Machine learning techniques such as "Gradient Boosting Machine" (GBM), an advanced learning algorithm that classifies the collected data from keystroke patterns and smartwatches attached to older adults, can categorize the severity level of CI with more than 94% accuracy, which could subsequently help in the prediction of CI (17). The advent of recent technologies can be considered a huge milestone in improving the mental health wellness of older adults to enable them to live independently and achieve an optimum level of functioning.…”

Detecting COVID-19 Status Using Chest X-ray Images and Symptoms Analysis by Own Developed Mathematical Model: A Model Development and Analysis Approach

“…Figures 13 and 14 are representing the correlations and p-values respectively. The role of "p" values and "r" values is to find out the best correlation and rank the feature to select the best features of the datasets [29]. The analysis of the correlation between the symptoms and COVID-19 status revealed that 10 out of 13 symptoms were significantly correlated to the COVID-19 disease.…”

Multidimensional digital biomarker of cognitive health: unobtrusive and continuous monitoring of cognitive changes using smartphones

“…This feasibility pilot study is based on the following three observations (Figure 3). First, cognitive impairment resultant from heterogeneous structural and functional changes in the brain in the affects latent cognitive abilities, which are reflected in observable cognitive performance (e.g., ability to engage and complete certain activities, the frequency of typing errors, and typing speed) 94,95,104,105,[96][97][98][99][100][101][102][103] .…”