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Mobile Networks and Applications
Published in:

21-08-2023

Hybrid IoT-Edge-Cloud Computing-based Athlete Healthcare Framework: Digital Twin Initiative

Authors: Shtwai Alsubai, Mohemmed Sha, Abdullah Alqahtani, Munish Bhatia

Published in: Mobile Networks and Applications | Issue 6/2023

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Abstract

Internet of Things (IoT) paradigm has been able to revolutionize ubiquitous healthcare in time-sensitive manner. Conspicuously, the current study proposes an intelligent athlete healthcare framework utilizing IoT-edge computing technology for effective medical care during excessive training and exercise sessions of athletes. Specifically, for analyzing real-time health data during exercises, probabilistic health state susceptibility is detected and quantified. The proposed framework incorporates a probabilistic Bayesian model for the effective classification of health-oriented vitals based on vulnerability. Furthermore, a Multi-scaled Long Term Memory (MLSTM) model is used for predictive purposes. Experimental simulations were conducted to determine validation aspects of the presented framework over challenging datasets. Comparative analysis with state-of-the-art decision-modeling techniques show that the presented framework is better in terms of statistical performance of Temporal Efficacy(62.85%), Classification Efficiency (Precision (95.63%), Specificity (92.32%), and Sensitivity (94.33%)), Predictive Accuracy (95.65%), and Stability (69%).
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Footnotes
2
Source:https://sports.ndtv.com/othersports/sports-ministry-to-spend-rs-190-cr-on-training-of-athletes-for-commonwealth-asian-games-2861956
 
3
Source:https://www.wtsp.com/article/sports/olympics/cost-of-being-an-olympian/67-7711bf2f-56bd-451f-ac8e-ac6afb5d5864
 
Literature
1.
Bhatia M, Sood SK (2020) Quantum computing-inspired network optimization for iot applications. IEEE Internet of Things Journal 7(6):5590–5598CrossRef
2.
Lv L, Wu Z, Zhang L, Gupta BB, Tian Z (2022) An edge-ai based forecasting approach for improving smart microgrid efficiency. IEEE Trans Ind Inform 18(11):7946–7954CrossRef
3.
Alzubi JA, Alzubi OA, Singh A, Ramachandran M (2022) Cloud-iiot-based electronic health record privacy-preserving by cnn and blockchain-enabled federated learning. IEEE Trans Ind Inform 19(1):1080–1087CrossRef
4.
Abbasi M, Yaghoobikia M, Rafiee M, Jolfaei A, Khosravi MR (2020) Efficient resource management and workload allocation in fog-cloud computing paradigm in iot using learning classifier systems. Comput Comm 153:217–228CrossRef
5.
Acampora G, Cook DJ, Rashidi P, Vasilakos AV (2013) A survey on ambient intelligence in healthcare. Proceedings of the IEEE 101(12):2470–2494CrossRef
6.
Albataineh H, Nijim M, Bollampall D (2020) The design of a novel smart home control system using smart grid based on edge and cloud computing. In: 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE). pp. 88–91, IEEE
7.
Alzubi OA, Qiqieh I, Alzubi JA (2022) Fusion of deep learning based cyberattack detection and classification model for intelligent systems. Clust Comput, pp. 1–12
8.
Movassagh AA, Alzubi JA, Gheisari M, Rahimi M, Mohan S, Abbasi AA, Nabipour N (2021) Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Human Comput, pp. 1–9,
9.
Alzubi OA, Alzubi JA, Shankar K, Gupta D (2021) Blockchain and artificial intelligence enabled privacy-preserving medical data transmission in internet of things. Trans Emerg Telecommun Techn 32(12):e4360CrossRef
10.
Monsalve JT, Arnold D, Yi WJ, Saniie J (2019) Design flow of wearable internet of things (IoT) smart workout tracking system. In: 2019 IEEE International Conference on Electro Information Technology (EIT). IEEE, pp 271–274
11.
Alzubi JA (2021) Blockchain-based lamport merkle digital signature: authentication tool in iot healthcare. Comput Commun 170:200–208CrossRef
12.
Bilbao-Jayo A, Cantero X, Almeida A, Fasano L, Montanaro T, Sergi I, Patrono L (2022) Location based indoor and outdoor lightweight activity recognition system. Electronics 11(3):360CrossRef
13.
Elayan H, Aloqaily M, Guizani M (2021) Digital twin for intelligent context-aware iot healthcare systems. IEEE Internet of Things Journal 8(23):16749–16757CrossRef
14.
Guimarães T, Quesado IA, Tavares I, Passos M, Duarte J, Santos MF, Silva Á (2022) Knowledge extraction from icu data using data visualization. In: Big Data Analytics and Artificial Intelligence in the Healthcare Industry. IGI Global, pp. 129–149
15.
James L (2021) Digital twins will revolutionise healthcare: Digital twin technology has the potential to transform healthcare in a variety of ways-improving the diagnosis and treatment of patients, streamlining preventative care and facilitating new approaches for hospital planning. Eng Tech 16(2):50–53CrossRef
16.
Rojas-Arce JL, Ortega-Maldonado EC (2021) The advent of the digital twin: A prospective in healthcare in the next decade. In: IFIP International Conference on Advances in Production Management Systems, Springer, pp. 249–255
17.
Ali M, Sadeghi M-R, Liu X, Miao Y, Vasilakos AV (2022) Verifiable online/offline multi-keyword search for cloud-assisted industrial internet of things. J Inf Secur App 65:103101
18.
Kobyakova OS, Starodubov VI, Kurakova NG, Tsvetkova LA (2021) Digital twins in healthcare: an assessment of technological and practical prospects. Annals of the Russian academy of medical sciences 76(5):476–487CrossRef
19.
Qahtan S, Yatim K, Zaidan A, Alsattar H, Albahri O, Zaidan B, Alamoodi A, Zulzalil H, Osman M, Mohammed R (2022) Novel multi security and privacy benchmarking framework for blockchain-based iot healthcare industry 4.0 systems. IEEE Trans Ind Inform
20.
King RC, Villeneuve E, White RJ, Sherratt RS, Holderbaum W, Harwin WS (2017) Application of data fusion techniques and technologies for wearable health monitoring. Med Eng Phys 42:1–12CrossRef
21.
Balamurugan M, Roy RR, Amanullah M, Shalini KS (2023) Inflexible wireless health surveillance sensor shields for health-care apps associated with the iot. Renewable Energy with IoT and Biomedical Applications, p. 7
22.
Liu Q, Mkongwa KG, Zhang C (2021) Performance issues in wireless body area networks for the healthcare application: A survey and future prospects. SN Applied Sciences 3(2):1–19CrossRef
23.
Aghdam ZN, Rahmani AM, Hosseinzadeh M (2021) The role of the internet of things in healthcare: Future trends and challenges. Comput Methods Prog Biomed 199:105903CrossRef
24.
Wang Z, Yuan Z, Xu H, Eghan C (2017) Intelligent private fitness system based on arm and hybrid internet of things. In: International Conference on Industrial IoT Technologies and Applications. Springer, 2017, pp. 91–101
25.
Oh C, Carducci B, Vaivada T, Bhutta ZA (2022) Interventions to promote physical activity and healthy digital media use in children and adolescents: a systematic review. Pediatrics 149(Supplement):6
26.
Wang X, Liu C, Sun L (2022) The design of sports games under the internet of things fitness by deep reinforcement learning. Comput Intell Neurosci, vol. 2022
27.
Heydari N, Hakakzadeh M, Manochehri Nejad M (2021) Model of economic development of sport industry using social networks. Sport Management Studies 13(68):30–63
28.
Bhatia M, Sood SK (2017) A comprehensive health assessment framework to facilitate iot-assisted smart workouts: A predictive healthcare perspective. Computers in Industry 92:50–66CrossRef
29.
Hassanalieragh M, Page A, Soyata T, Sharma G, Aktas M, Mateos G, Kantarci B, Andreescu S (2015) Health monitoring and management using internet-of-things (iot) sensing with cloud-based processing: Opportunities and challenges. In: 2015 IEEE international conference on services computing. IEEE, pp. 285–292
30.
Kalabakov S, Gjoreski M, Gjoreski H, Gams M (2021) Analysis of deep transfer learning using deepconvlstm for human activity recognition from wearable sensors. Informatica, vol. 45, no. 2
31.
Tao F, Zhang H, Liu A, Nee AY (2018) Digital twin in industry: State-of-the-art. IEEE Trans Ind Inf 15(4):2405–2415CrossRef
32.
Alloghani M, Subair S, Thron C (2022) A systematic review on application of data mining techniques in healthcare analytics and data-driven decisions. Artificial Intelligence for Data Science in Theory and Practice, pp. 119–138
33.
Garcia-Valverde T, Muñoz A, Arcas F, Bueno-Crespo A, Caballero A (2014) Heart health risk assessment system: a nonintrusive proposal using ontologies and expert rules. BioMed research international, vol. 2014
34.
Wang S-H, Satapathy SC, Xie M-X, Zhang Y-D (2023) Elucnn for explainable covid-19 diagnosis. Soft Computing, pp. 1–17
35.
Zhang Y, Hong J, Chen S (2023) Medical big data and artificial intelligence for healthcare, p. 3745
36.
Chen J, Chen H, Wu Z, Hu D, Pan JZ (2017) Forecasting smog-related health hazard based on social media and physical sensor. Inf Syst 64:281–291CrossRef
37.
Grieves M (2014) Digital twin: manufacturing excellence through virtual factory replication. White paper 1:1–7
38.
Glaessgen E, Stargel D (2012) The digital twin paradigm for future nasa and us air force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, p. 1818
39.
Garg H, Sharma B, Shekhar S, Agarwal R (2022) Spoofing detection system for e-health digital twin using efficientnet convolution neural network. Multimed Tools Appl, pp. 1–16
40.
Legaspi J, Bhada SV (2022) Introducing digital doppelgängers for healthcare policy analysis. In: Recent Trends and Advances in Model Based Systems Engineering. Springer, pp. 25–33
41.
Gregory D (2022) Healthcare goes digital: Designing for the convergence of the digital and physical environment-implications for design professionals. p. 19375867221082780
42.
Kamel Boulos MN, Zhang P (2021) Digital twins: From personalised medicine to precision public health. J Personalized Med 11(8):745CrossRef
Metadata
Title
Hybrid IoT-Edge-Cloud Computing-based Athlete Healthcare Framework: Digital Twin Initiative
Authors
Shtwai Alsubai
Mohemmed Sha
Abdullah Alqahtani
Munish Bhatia
Publication date
21-08-2023
Publisher
Springer US
Published in
Mobile Networks and Applications / Issue 6/2023
Print ISSN: 1383-469X
Electronic ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-023-02200-z

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