IoT-based intelligent system For Alzheimer's Disease Detection & Monitoring
Abstract
This research is based on two areas related to Alzheimer's disease, the first is the early detection and diagnosis of Alzheimer's disease using deep learning techniques and its various algorithms, and the second relates to how to monitor and follow up on Alzheimer's disease using the Internet of Things (IOT). In this paper, a new diagnosis based on deep machine learning and monitoring of diseases similar to Alzheimer's is proposed. Diagnosis of Alzheimer's-like diseases is achieved through deep learning magnetic resonance imaging (MRI) analysis followed by an activity tracking framework to monitor people's activities in daily life using wearable inertial sensors. Activity monitoring provides a framework for assistance in activities of daily living and assessment of patient deterioration based on activity level. The results of Alzheimer's diagnosis show an improvement of up to 86.34% with respect to current known techniques. Furthermore, greater than 95% accuracy was achieved for classifying activities of daily living, which is very encouraging in terms of looking at the activity profile of the subject.
References
[2] Vermesan O, Friess P, editors. Internet of things applications-from research and innovation to market deployment. CRC Press; 2022 Sep 1.
[3] Sharma R, Goel T, Tanveer M, Murugan R. FDN-ADNet: Fuzzy LS-TWSVM based deep learning network for prognosis of the Alzheimer’s disease using the sagittal plane of MRI scans. Applied Soft Computing. 2022 Jan 1; 115:108099.
[4] Lee G, Nho K, Kang B, Sohn KA, Kim D. Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Scientific reports. 2019 Feb 13;9(1):1-2.
[5] Sharma S, Guleria K, Tiwari S, Kumar S. A deep learning-based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans. Measurement: Sensors. 2022 Oct 5:100506.
[6] Odusami M, Maskeliūnas R, Damaševičius R. An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging. Sensors. 2022 Jan 19;22(3):740.
[7] Mehmood A, Maqsood M, Bashir M, Shuyuan Y. A deep Siamese convolution neural network for multi-class classification of Alzheimer disease. Brain sciences. 2020 Feb 5;10(2):84.
[8] Feng W, Halm-Lutterodt NV, Tang H, Mecum A, Mesregah MK, Ma Y, Li H, Zhang F, Wu Z, Yao E, Guo X. Automated MRI-based deep learning model for detection of Alzheimer’s disease process. International Journal of Neural Systems. 2020 Jun 27;30(06):2050032.
[9] Benyoussef, E.M.; Elbyed, A.; El Hadiri, H. 3D MRI classification using KNN and deep neural network for Alzheimer’s disease diagnosis. In International Conference on Advanced Intelligent Systems for Sustainable Development; Tangier, Morocco, 12–14 July, Springer: Cham, Switzerland, 2019; pp. 154–158.
[10] Stoyanova M., Nikoloudakis Y., Panagiotakis S., Pallis E., Markakis E. K. A survey on the Internet of Things (IoT) forensics: challenges, approaches, and open issues. IEEE Communication Surveys and Tutorials. 2020;22(2):1191–1221. doi: 10.1109/COMST.2019.2962586.
[11] Khan S. S., Ye B., Taati B., Mihailidis A. Detecting agitation and aggression in people with dementia using sensors—a systematic review. Alzheimer's & Dementia. 2018;14(6):824–832. doi: 10.1016/j.jalz.2018.02.004.
[12] Mahendran N, PM DR. A deep learning framework with an embedded-based feature selection approach for the early detection of Alzheimer's disease. Computers in Biology and Medicine. 2022 Feb 1; 141:105056.
Authors
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