A Proposed CNN Approach for Remote Monitoring of Elderly
Keywords:
smart space; Internet of Things; monitoring systems; deep learning; convolutional neural network; machine learning; healthcare.Abstract
The emergence of Smart Housing for Health aims to restore autonomy to elderly people with chronic illnesses, allowing them to remain at home. This concept requires an intelligent system that collects residents' data to monitor their activities and provide personalized services. This project addresses the issue of monitoring elderly people in a smart home environment while preserving their dignity and freedom. The proposed solution relies on integrating a deep learning-based system within the server, enabling the analysis of resident data and making real-time health-related decisions to adapt the provided services accordingly.
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Anita, Prasad. (2024). 1. Older adults care in demographic change: socio-economic challenges and opportunities in social science research. doi: 10.58532/v3bjso19p1ch3
Zihao, Wei. (2023). 3. The impact of population aging on economic growth Based on a case analysis in Beijing. Highlights in Business, Economics and Management, doi: 10.54097/hbem.v13i.8640
Klára, Rybenská., Lenka, Knapová., Kamil, Janiš., Jitka, Kühnová., Richard, Cimler., Steriani, Elavsky. (2024). 1. SMART technologies in older adult care: a scoping review and guide for caregivers. Journal of enabling technologies, doi: 10.1108/jet-05-2023-0016
Jiebing, Zhu., Di, Wang., Yanmei, Zhao. (2024). 3. Design of smart home environment based on wireless sensor system and artificial speech recognition. doi: 10.1016/j.measen.2024.101090
Dohr A, Modre-Opsrian R, Drobics M, Hayn D, Schreier G (2010) The internet of things for ambient assisted living. In: 2010 seventh international conference on information technology: new generations (ITNG), pp 804–809
Oresko JJ, Jin Z, Cheng J, Huang S, Sun Y, Duschl H, Cheng AC (2010) A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Trans Inf Technol Biomed 14(3):734–40
Gupta MS, Patchava V, Menezes V (2015) Healthcare based on IoT using Raspberry Pi. In: 2015 international conference on green computing and internet of things (ICGCIoT). IEEE, pp 796–799)
Kreps, G. L., & Neuhauser, L. (2010). New directions in eHealth communication: opportunities and challenges. Patient education and counseling, 78(3), 329-336.
Patel S, Park H, Bonato P, Chan L, Rodgers M (2012) A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil 9 (1):21
Pham M, Mengistu Y, Do HM, Sheng W (2016) Cloud-Based Smart Home Environment (CoSHE) for home healthcare. In: 2016 IEEE international conference on automation science and engineering (CASE). IEEE, pp 483–488
Nweke, H.F.; Teh, Y.W.; Al-garadi, M.A.; Alo, U.R. Deep Learning Algorithms for Human Activity Recognition Using Mobile and Wearable Sensor Networks: State of the Art and Research Challenges. Expert Syst. Appl. 2018, 105, 233–261.
LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521, 436–444
LeCun, Y.; Boser, B.; Denker, J.S.; Henderson, D.; Howard, R.E.; Hubbard, W.; Jackel, L.D. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput. 1989, 1, 541–551.
Zhang, Q.; Yang, L.T.; Chen, Z.; Li, P. A Survey on Deep Learning for Big Data. Inf. Fusion 2018, 42, 146–157
Logan, A.G, McIsaac, Tisler, A., Irvine, Saunders, A et Dunai, A, «Mobile phone–based remote patient monitoring system for management of hypertension in diabetic patients.Am. J. Hypertens.,» p. 942–948, 2007
Uddin, M.; Khaksar, W.; Torresen, J. Ambient sensors for elderly care and independent living: A survey. Sensors 2018
Saha, Saha, A.K, Chatterjee, A, Agrawal, S, Saha, A, Kar, A et Saha, H.N, «Advanced IOT based combined remote health monitoring, home automation and alarm system. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). IEEE,,» p. 602–606, 2018. January.
Quer, G, Nikzad, N, Chieh, A, Normand, A, Vegreville, M, Topol, E.J et Steinhubl, S.R,« Home monitoring of blood pressure: short-term changes during serial measurements
for 56398 Subjects. IEEE J. Biomed. Health. Inf. 22 (5), 1691– 1698.),» 2017.
Panicker, N.V et Kumar, A.S, «Development of a blood pressure monitoring system for home health application. In: 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015] (pp. 1-4). IEEE.),» 2015.
G. A. C. Sharanya et K. Reddy, «Health monitoring device. In: 2017 2nd International Conference on Communication and Electronics Systems (ICCES). IEEE,» p. 668–671, 2017. October.
Saiteja P C, Gahangir H, Ayush G, Anupama B, Sayantan B, Devottam G et Sanju M T, «Smart home health monitoring system for predicting type 2 diabetes and hypertension, Journal of King Saud University – Computer and Information Sciences,» p. 2022.
Li, Wang. (2024). 1. Development of a smart health monitoring system for elderly care. Applied and Computational Engineering, doi: 10.54254/2755-2721/45/20241022
Hao, Zeng. (2023). 6. Design of IoT Home System for the Elderly based on Different Scenarios. Transactions on computer science and intelligent systems research, doi: 10.62051/vydm8391
Activity Monitoring and Alert System for Elderly People in Smart Homes. doi: 10.1109/aisp57993.2023.10134876
(2022). Smart Health Monitoring System For The Elderly. doi:0.1109/icatiece56365.2022.10047781
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