Abstract
Alzheimer’s Disease (AD) is an invasive neurodegenerative disorder that has no cure. The treatment of AD is based on drugs, which must be administered in the early stages of the development of the disease. Therefore, early diagnosis of the disease is of great significance for the efficiency of the treatment. AD is currently diagnosed by the provision of a professional, conducting medical tests, which is not easily accessible. In this paper, we introduce Caregiver, an application that takes initiative by administering Clock Drawing Test (CDT) on mobile devices and facilitates administration of the test in a fun and simple digital environment. Caregiver gives suggestions to its users to seek professional help for treatment of AD based on the score they get from computer feedback on the clock drawings. Conducting the CDT step by step through gamification, Caregiver provides essential healthcare to people that don’t have access to medical faculties. Saving its users data, Caregiver also provides an insight on the progression of the cognitive impairment if present. Its user-friendly interface enables its users to actively interact with the application and obtain more accurate results than conducting the test by themselves. Caregiver evaluates visual inputs of the test using a weighted combination of two different classification models, which decreases the total time to conduct the test, hence providing early diagnosis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
NHS Choices: NHS. https://www.nhs.uk/conditions/alzheimers-disease/
Zeinab, B., Karaman, R.: Comprehensive Review on Alzheimer's Disease: Causes and Treatment. MDPI, Multidisciplinary Digital Publishing Institute (2020). https://www.mdpi.com/1420-3049/25/24/5789
10 Early Signs and Symptoms of Alzheimer’s. Alzheimer's Disease and Dementia. https://www.alz.org/alzheimers-dementia/10_signs
NHS Choices: NHS. https://www.nhs.uk/conditions/alzheimers-disease/causes/
How is Alzheimer's Disease Treated? National Institute on Aging, U.S. Department of Health and Human Services. https://www.nia.nih.gov/health/how-alzheimers-disease-treated
Jill, R., Langerman, H.: Alzheimer's Disease - Why We Need Early Diagnosis. Degenerative Neurological and Neuromuscular Disease, Dove (2019). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6935598/
Raman, M., Avidan, A.Y.: Neurology. Clinical Men's Health, W.B. Saunders (2009). https://www.sciencedirect.com/science/article/pii/B9781416030003100139
Deborah, A.C.-W., et al.: Brain structural and cognitive correlates of clock drawing performance in Alzheimer’s disease. J. Int. Neuropsychol. Soc. 5, 502–509 (1999)
Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.: Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 1015–1018 (2014). https://doi.org/10.1109/ISBI.2014.6868045
Xu, M., David, L.-S., Pilar, G., Fernando, M., Li, Q., Dimitrios, P.: A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks (2020)
da Silva, I.R.R., et al.: Deep learning for early diagnosis of alzheimer's disease: a contribution and a brief review. Deep Learning for Data Analytics (2020). https://www.sciencedirect.com/science/article/pii/B9780128197646000053. Accessed 3 Jan 2022
Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., Song, X.: Early detection of Alzheimer’s disease using Magnetic Resonance Imaging: a novel approach combining convolutional neural networks and ensemble learning. Frontiers. https://www.frontiersin.org/articles/10.3389/fnins.2020.00259/full. Accessed 3 Jan 2022
Yann, L., Cortes, C., Burges, C.J.C.: MNIST handwritten digit database. N.p. (2021). http://yann.lecun.com/exdb/mnist/
Shuqing, C., et al.: Automatic dementia screening and scoring by applying deep learning on clock-drawing tests. Sci. Reports 10(1) (2020). https://doi.org/10.1038/s41598-020-74710-9
Introduction to Cognitive Testing. PsychDB (2021). https://www.psychdb.com/cognitive-testing/introduction
Isabelle, R., et al.: Quantitative and qualitative analyses of clock drawings in Alzheimer’s and Huntington’s disease. Brain Cogn. 18, 70–87 (1992)
Kenneth, I.S., et al.: Clock-drawing and dementia in the community: a longitudinal study. Int. J. Geriatric Psych. 8(10) (1993). https://doi.org/10.1002/gps.v8:10
Tuokko, H., et al.: The clock test: a sensitive measure to differentiate normal elderly from those with Alzheimer Disease. J. Anim. Physiol. Nutr. 40(6), 579–584 (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Taki, R., Bahar, R.R., Kocak, A.E., Yalcin, S. (2022). Caregiver: An Application for the First Step in Alzheimer’s Disease Early Diagnosis. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1580. Springer, Cham. https://doi.org/10.1007/978-3-031-06417-3_83
Download citation
DOI: https://doi.org/10.1007/978-3-031-06417-3_83
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06416-6
Online ISBN: 978-3-031-06417-3
eBook Packages: Computer ScienceComputer Science (R0)