Assessing Spatial Navigation Task Performance: Using Mobile Applications to Detect Decline of Working Memory in the Aging Population
Device application software development has significantly evolved over the last 35 years. After the emergence of rudimentary home phones and basic gaming software, highly interactive applications have slowly replaced these predecessors, and are now capable of algorithmic learning based on the user’s actions. The current consumer market preference points to full mobile use of such applications via Android mobile and Apple IOS development platforms. Utilizing such technology enables endless possibilities. We chose to explore mobile application tools to generate new methods for early and non-invasive detection of cognitive decline in aging.
Early markers of aging-related cognitive decline can be detected in clinical settings. These markers include spatial navigation deficits and alterations in brain waves. However, these measures are not taken in naturalistic settings under conditions of normal behavior. It is, therefore, our interest to track navigation ability during normal daily behavior to get a better idea of the application of clinical findings to real-world settings. Our device application will allow for tracking and recording of the participant’s navigation routes, calculation of the “typical” path from the aggregate of the individual routes, and detection of behavioral anomalies, i.e. leaving the typical route unexpectedly. When the user exceeds a deviation threshold, our system will send a text message to gather information as to the reason for the anomaly. Research participant responses of current status assist in identifying reasons for performance errors such as traffic, weather conditions, or forgetfulness, etc.. This will be accomplished via XCODE generated IOS/Android navigation data that are received and compiled through a Firebase server and processed through back-end Python averaging and outlier modeling.
We hypothesize that early detection of spatial navigation impairment will transfer from the laboratory to the real world. The long-term hope is that this device application will enable large-scale data collection and benefit those at risk for advancing disease.
Copyright (c) 2022 CHANELE GREENE, SEUNG AH (ANNABELLE) CHOI, SHAUNAK GANJU, SURESH PATIL, Theodore Dumas
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