Abstract
Alzheimer’s disease (AD) refers to a neurodegenerative chronic disease. Difficulties to recall current events, daily tasks schedules, eye vision problem, fail to maintain daily routine, and problems to read and speak new languages are the most common symptoms (early-mid level) of AD. Magnetic resonance imaging (MRI) is very popular for detection of AD. There are numerous research works which are available for early detection of AD. But we have found lack of concentration to detect AD and assist AD patients using Internet of things (IoT) devices inside smart home focusing 5G wireless network. In this paper, we have proposed AlziHelp: An Alzheimer disease detection and assistive system inside smart home focusing 5G using IoT and machine learning approaches. In our system, AD detection can be done easily using smart IoT devices inside smart home in 5G environment. Also the system is capable to assist AD patients using machine learning (ML) approaches. Monitoring daily tasks, reaction time to take an actions, mismatches in serials of actions will be taken as input in our system and using k-nearest neighbor (K-NN), our system can easily detect AD. Also, the system can assist an AD patient to perform his/her daily tasks by predicting events and actions. We strongly believe that AzliHelp can contribute to detect AD and assist people with AD so that they can live a normal life inside home.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fuse H, Oishi K, Maikusa N, Fukami T, Initiative JADN (2018) Detection of Alzheimer’s disease with shape analysis of MRI images. In: 2018 joint 10th international conference on soft computing and intelligent systems (SCIS) and 19th international symposium on advanced intelligent systems (ISIS), Toyama, Japan, pp 1031–1034
Thakare P, Pawar VR (2016) Alzheimer disease detection and tracking of Alzheimer patient. In: 2016 international conference on inventive computation technologies (ICICT), Coimbatore
Roopaei M, Rad P, Prevost JJ (2018) A wearable IoT with complex artificial perception embedding for Alzheimer patients. In: 2018 world automation congress (WAC), Stevenson, WA
Khan A, Usman M (2015) Early diagnosis of Alzheimer’s disease using machine learning techniques: a review paper. In: 2015 7th international joint conference on knowledge discovery, knowledge engineering and knowledge management (IC3K), Lisbon, pp 380–387
Sigwele T, Hu YF, Ali M, Hou J, Susanto M, Fitriawan H (2018) Intelligent and energy efficient mobile smartphone gateway for healthcare smart devices based on 5G. In: 2018 IEEE global communications conference (GLOBECOM), Dec 2018, Abu Dhabi, UAE
Shamim Hossain M, Muhammad G (2018) Emotion-aware connected healthcare big data towards 5G. IEEE Internet Things J 5(4), Aug 2018
Lema MA, Laya A, Mahmoodi T, Cuevas M, Sachs J, Markendahl J, Dohler M (2017) Business case and technology analysis for 5G low latency applications. IEEE Access, PP(99)
Aldaej A, Tariq U (2018) IoT in 5G Aeon: an inevitable fortuity of next generation healthcare. In: 2018 1st international conference on computer applications and information security (ICCAIS), Apr 2018, Riyadh, Saudi Arabia. https://doi.org/10.1109/cais.2018.8441986
Surendran D, Janet J, Prabha D, Anisha E (2018) A study on devices for assisting Alzheimer patients. In: 2018 2nd international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, pp 620–625
Aljehani SS, Alhazmi RA, Aloufi SS, Aljehani BD, Abdulrahman R (2018) iCare: applying IoT technology for monitoring Alzheimer’s patients. In: 2018 1st international conference on computer applications and information security (ICCAIS), Riyadh, pp 1–6
Helmy J, Helmy A (2016) The Alzimio App for Dementia, Autism & Alzheimer’s: using novel activity recognition algorithms and geofencing. In: 2016 IEEE international conference on smart computing (SMARTCOMP), St. Louis, MO, pp 1–6
Alvarez F et al (2017) Multimodal monitoring of Parkinson’s and Alzheimer’s patients using the ICT4LIFE platform. In: 2017 14th IEEE international conference on advanced video and signal based surveillance (AVSS), Lecce, pp 1–6
Alvarez F et al (2018) Behavior analysis through multimodal sensing for care of Parkinson’s and Alzheimer’s patients. In: IEEE multiMedia, vol 25, no 1, pp 14–25, Jan–Mar 2018
Mainetti L, Patrono L, Rametta P (2016) Capturing behavioral changes of elderly people through unobtrusive sensing technologies. In: 2016 24th international conference on software, telecommunications and computer networks (SoftCOM), Split, pp 1–3
Sharma J, Kaur S (2017) Gerontechnology—the study of alzheimer disease using cloud computing. In: 2017 international conference on energy, communication, data analytics and soft computing (ICECDS), Chennai, pp 3726–3733
Din S, Paul A, Ahmed A, Rho S (2016) Emerging mobile communication technologies for healthcare system in 5G network. In: 2016 IEEE 14th international conference on dependable, autonomic and secure computing, 14th international conference on pervasive intelligence and computing, 2nd international conference on big data intelligence and computing and cyber science and technology c(DASC/PiCom/DataCom/CyberSciTech), August 2016, Auckland, New Zealand
Jones RW, Katzis K (2018) 5G and wireless body area networks. In: 2018 IEEE wireless communications and networking conference workshops (WCNCW), April 2018, Barcelona, Spain. https://doi.org/10.1109/wcncw.2018.8369035
Healy M, Walsh P (2017) Detecting demeanor for healthcare with machine learning. In: 2017 IEEE international conference on bioinformatics and biomedicine (BIBM), Nov 2017, USA
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ibrahim Mamun, M., Rahman, A., Mridha, M.F., Hamid, M.A. (2021). AlziHelp: An Alzheimer Disease Detection and Assistive System Inside Smart Home Focusing 5G Using IoT and Machine Learning Approaches. In: Singh Pundir, A.K., Yadav, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0167-5_12
Download citation
DOI: https://doi.org/10.1007/978-981-16-0167-5_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0166-8
Online ISBN: 978-981-16-0167-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)