CogTrack: A Proof of Concept for Cognition Tracker
Dementia and pathological cognitive impairment are global public health challenges. Given that there is no reliable medical cure for dementia, it is critical to develop tools that forewarn of cognitive deterioration. Usually, dementia begins 1-2 decades before symptoms manifest; this time window pro...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10804776/ |
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| author | Yesoda Bhargava Kanthi Kumar Kattupalli Veeky Baths |
| author_facet | Yesoda Bhargava Kanthi Kumar Kattupalli Veeky Baths |
| author_sort | Yesoda Bhargava |
| collection | DOAJ |
| description | Dementia and pathological cognitive impairment are global public health challenges. Given that there is no reliable medical cure for dementia, it is critical to develop tools that forewarn of cognitive deterioration. Usually, dementia begins 1-2 decades before symptoms manifest; this time window provides an excellent opportunity to identify cognitive deterioration early, which can enable more timely interventions. In this context, cognition tracking offers hope. The growth in wearable sensor-based devices has enabled a wide range of real-time physiological and activity metrics to be recorded. Cognition tracking is a natural progression to this growth and can prove priceless for early detection of dementia. In this work, we present a proof-of-concept of a cognition tracker, CogTrack. CogTrack is a mobile app that visualizes cognitive performance scores obtained from virtual-reality and mobile-based goal-oriented games designed and validated by us. The practical and clinical significance of CogTrack is discussed using two use cases: 1) Early indication of cognitive deterioration and 2) Tracking the impact of cognitive rehabilitation. A four-tier model is presented to illustrate the beneficiaries of CogTrack. The advantages and limitations of CogTrack are discussed in the context of available mobile applications, devices, and tools that measure cognitive performance. Additionally, we discuss how CogTrack may be advanced in the future and reflect on critical issues related to data privacy, confidentiality, ethics, scalability, and socio-medical factors which influence CogTrack adoption and acceptability. In summary, we show how an integrated system of goal-oriented games and smartphone app may be used for longitudinal cognitive assessment and tracking. |
| format | Article |
| id | doaj-art-b508ea8421b84bc38399e93a3bbddec8 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b508ea8421b84bc38399e93a3bbddec82025-08-20T02:56:47ZengIEEEIEEE Access2169-35362024-01-011219460919462910.1109/ACCESS.2024.351922010804776CogTrack: A Proof of Concept for Cognition TrackerYesoda Bhargava0https://orcid.org/0000-0002-7693-7153Kanthi Kumar Kattupalli1https://orcid.org/0009-0008-7508-1454Veeky Baths2https://orcid.org/0000-0001-9980-5738Department of Biological Sciences, Cognitive Neuroscience Laboratory, BITS Pilani K. K. Birla Goa Campus, Sancoale, Goa, IndiaDepartment of Biological Sciences, Cognitive Neuroscience Laboratory, BITS Pilani K. K. Birla Goa Campus, Sancoale, Goa, IndiaDepartment of Biological Sciences, Cognitive Neuroscience Laboratory, BITS Pilani K. K. Birla Goa Campus, Sancoale, Goa, IndiaDementia and pathological cognitive impairment are global public health challenges. Given that there is no reliable medical cure for dementia, it is critical to develop tools that forewarn of cognitive deterioration. Usually, dementia begins 1-2 decades before symptoms manifest; this time window provides an excellent opportunity to identify cognitive deterioration early, which can enable more timely interventions. In this context, cognition tracking offers hope. The growth in wearable sensor-based devices has enabled a wide range of real-time physiological and activity metrics to be recorded. Cognition tracking is a natural progression to this growth and can prove priceless for early detection of dementia. In this work, we present a proof-of-concept of a cognition tracker, CogTrack. CogTrack is a mobile app that visualizes cognitive performance scores obtained from virtual-reality and mobile-based goal-oriented games designed and validated by us. The practical and clinical significance of CogTrack is discussed using two use cases: 1) Early indication of cognitive deterioration and 2) Tracking the impact of cognitive rehabilitation. A four-tier model is presented to illustrate the beneficiaries of CogTrack. The advantages and limitations of CogTrack are discussed in the context of available mobile applications, devices, and tools that measure cognitive performance. Additionally, we discuss how CogTrack may be advanced in the future and reflect on critical issues related to data privacy, confidentiality, ethics, scalability, and socio-medical factors which influence CogTrack adoption and acceptability. In summary, we show how an integrated system of goal-oriented games and smartphone app may be used for longitudinal cognitive assessment and tracking.https://ieeexplore.ieee.org/document/10804776/Android developmentartificial intelligencecognitive assessmentcognitive rehabilitationcognition trackerdata analysis |
| spellingShingle | Yesoda Bhargava Kanthi Kumar Kattupalli Veeky Baths CogTrack: A Proof of Concept for Cognition Tracker IEEE Access Android development artificial intelligence cognitive assessment cognitive rehabilitation cognition tracker data analysis |
| title | CogTrack: A Proof of Concept for Cognition Tracker |
| title_full | CogTrack: A Proof of Concept for Cognition Tracker |
| title_fullStr | CogTrack: A Proof of Concept for Cognition Tracker |
| title_full_unstemmed | CogTrack: A Proof of Concept for Cognition Tracker |
| title_short | CogTrack: A Proof of Concept for Cognition Tracker |
| title_sort | cogtrack a proof of concept for cognition tracker |
| topic | Android development artificial intelligence cognitive assessment cognitive rehabilitation cognition tracker data analysis |
| url | https://ieeexplore.ieee.org/document/10804776/ |
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