Title
MobileCogniTracker: A mobile experience sampling tool for tracking cognitive behaviour
Abstract
As the population ages, cognitive decline is becoming a worldwide threat to older adults' independence and quality of life. Cognitive decline involves problems with memory, language, thinking and judgement, thus severely compromising multiple aspects of people's everyday life. Diagnosis of cognitive disorders is currently performed through clinical questionnaire-based assessments, which are typically conducted by medical experts once symptoms appear. Digital technologies can help providing more immediate, pervasive and seamless assessment, which could, in turn, allow for much earlier diagnosis of cognitive disorders and decline. In this work, we present MobileCogniTracker, a digital tool for facilitating momentary, seamless and ubiquitous clinically-validated cognitive measurements. The proposed tool develops digital cognitive tests in the form of multimedia experience sampling questionnaires, which can run on a smartphone and can be scheduled and assessed remotely. The tool further integrates the digital cognitive experience sampling with passive smartphone sensor data streams that may be used to study the interplay of cognition and physical, social and emotional behaviours. The Mini-Mental State Examination test, a clinical questionnaire extensively used to measure cognitive disorders, has been particularly implemented here to showcase the possibilities offered by our tool. A usability test showed the tool to be usable for performing digital cognitive examinations, and that cognitively unimpaired persons in the relevant age-group are capable of performing such digital examination. A qualitative expert-driven validation also shows a high inter-reliability between the digital and pencil-and-paper version of the test.
Year
DOI
Venue
2019
10.1007/s12652-018-0827-y
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
Keywords
Field
DocType
Cognitive assessment,Smartphone,Mobile sensing,Human behaviour,mHealth
Cognitive Assessment System,Mobile sensing,Computer science,Experience sampling method,Scopus,mHealth,Human–computer interaction,Artificial intelligence,Cognition,Machine learning
Journal
Volume
Issue
ISSN
10.0
SP6.0
1868-5137
Citations 
PageRank 
References 
1
0.37
11
Authors
5