Abstract
Alzheimer’s disease is a common and tremendous growing neuron-based disease over the globe. Several biomedical devices for the detection and prediction of Alzheimer’s disease with IOT-based remote monitoring system were developed. For optimization and accurate Alzheimer’s disease prediction, a new wearable IOT framework is designed and developed by integrating electroencephalogram (EEG) and electromyography (EMG) sensors by interfacing machine learning and neural network algorithms. Data collected from EEG and EMG wearable sensors devices is stored on the cloud database. Machine learning and AI algorithm are applied to analyze on stored data to diagnosis Alzheimer’s disease and neural network algorithm applied for optimization of diagnosis of Alzheimer’s disease. Obtained result is shared with patients’ caretaker and doctor through designed IOT system to avoid and rescue the patient to escalate the next stage of disease. System is also designed to interact and monitor the patient.
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References
Mattson MP (2004) Pathways towards and away from Alzheimer’s disease. Nature. https://doi.org/10.1038/nature02621 Review Article (2004/08/05/online 430-631 Macmillan Magazines Ltd.)
Meek PD, McKeithan EK, Schumock GT (1998) Economic considerations in Alzheimer’s disease. Pharmacotherapy J Human Pharmacol Drug Ther 18:68–73. https://doi.org/10.1002/j.1875-9114.1998.tb03880.x
Wortmann M (2012) Dementia: a global health priority—highlights from an ADI and World Health Organization report Alzheimer’s Research & Therapy 4 1758–9193. https://doi.org/10.1186/alzrt143: Förstl H, Kurz A (1999) Clinical features of Alzheimer’s disease. Eur Arch Psychiatry Clin Neurosci 249:288–290
Thakare P, Pawar VR (2016) Alzheimer disease detection and tracking of Alzheimer patient. In: 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, pp 1–4. https://doi.org/10.1109/inventive.2016.7823286
Kulkarni N, Bairagi VK (2014) Diagnosis of Alzheimer disease using EEG signals. Int J Eng Res Tech 3(4):1835–1838
Traimboli LR, Falk TH, Fraga FJ, AnghinahR, Lorena AC (2011) EEG sepectro-temporal modulation energy: a new feature for automated diagnosis of Alzheimer disease. In: Proceedings of international conference of the IEEE-EMBC, vol 1, Boston, USA, pp 3828-3831 (2011)
Shenvi P, Baheria P, Jose S, Kumar S, Nayak JS (2016) Wearable tracking device for Alzheimer’s patients: a survey. 3(4):147–151. ISSN-2349-5162
Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2018) Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Comput 21(1):681–690
Al-khafajiy M, Baker T, Chalmers C, Asim M, Kolivand H, Fahim M, Waraich A (2019) Remote health monitoring of elderly through wearable sensors. Multimedia Tools Appl 1–26
Kulkarni NN, Bairagi VK (2015) Electroencephalogram based diagnosis of Alzheimer disease. In: 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), pp 1–5. https://doi.org/10.1109/isco.2015.7282275
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Pavitra, B., Singh, D.N., Sharma, S.K. (2020). Predictive and Interactive IOT Diagnosis System with AI and ML Tools: Review. In: Chillarige, R., Distefano, S., Rawat, S. (eds) Advances in Computational Intelligence and Informatics. ICACII 2019. Lecture Notes in Networks and Systems, vol 119. Springer, Singapore. https://doi.org/10.1007/978-981-15-3338-9_2
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DOI: https://doi.org/10.1007/978-981-15-3338-9_2
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