Abstract
In the last years, the Internet of Things (IoT) has pilot the vision of a smarter world into reality with a massive amount of data and numerous services. The development of smart sensorial media and devices is getting remarkable attention from academia, government, industry, and healthcare communities. IoT-powered systems produce valuable sources of information and can transform healthcare. With the increase of healthcare services in non-clinical environments, which use vital signs provided by sensors and devices connected to patients, the need to mine and process the physiological measurements is growing significantly. The utilization of IoT to support healthcare is possible thanks to the artificial intelligence (AI). AI techniques, like natural language processing, data analytics, machine learning, and its sub-category deep learning, offer immense opportunities including disease diagnosis and monitoring, clinical workflow augmentation, and hospital optimization. The synergy between the IoT and AI is promising to monitor state of health of patients and to move upon traditional healthcare structures. Accompanied by communication technologies, cloud computing, and big data, it led to the emergence of the Smart Health concept. The chapter exhibits a literature review conducted to determine the most important technologies, methodologies, algorithms, and models for smart health systems. In addition, the main benefits and challenges of smart health were explored.
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References
Abawajy, J.H., Hassan, M.M.: Federated Internet of things and cloud computing pervasive patient health monitoring system. IEEE Commun. Mag. 55(1), 48–53 (2017)
Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.-H., Liu, B.: Predicting flu trends using twitter data. In: 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 702–707 (2011)
Agapito, G., Calabrese, B., Care, I., Falcone, D., Guzzi, P.H., Ielpo, N., Lamprinoudi, T., Milano, M., Simeoni, M. and Cannataro, M.: Profiling basic health information of tourists: towards a recommendation system for the adaptive delivery of medical certified nutrition contents. In: 2014 International Conference on High Performance Computing & Simulation (HPCS), pp. 616–620. IEEE, Piscataway (2014)
Agapito, G., Calabrese, B., Guzzi, P.H., Cannataro, M., Simeoni, M., Caré, I., Lamprinoudi, T., Fuiano, G., Pujia, A.: DIETOS: A recommender system for adaptive diet monitoring and personalized food suggestion. In: 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 1–8. IEEE, Piscataway (2016)
Agapito, G., Guzzi, P.H., Cannataro, M.: Parallel extraction of association rules from genomics data. Appl. Math. Comput. 350, 434–446 (2019)
Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M. and Alizadeh, M.: The application of Internet of things in healthcare: a systematic literature review and classification. Univ. Access Inf. Soc. 18(4), 837–869 (2019)
Akmandor, A.O., Jha, N.K.: Keep the stress away with soda: Stress detection and alleviation system. IEEE Trans. Multi-Scale Comput. Syst. 3(4), 269–282 (2017)
Almotiri, S.H., Khan, M.A. and Alghamdi, M.A.: Mobile health (m-health) system in the context of IoT. In: 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), pp. 39–42 (2016)
Bragg, D.D., Edis, H., Clark, S., Parsons, S.L., Perumpalath, B., Lobo, D.N. and Maxwell-Armstrong, C.A.: Development of a telehealth monitoring service after colorectal surgery: a feasibility study. World J. Gastroenterol. 9, 193 (2017). Open Access article. OL 18.04.2018
Cho, K.J., Asada, H.H.: Wireless, battery-less stethoscope for wearable health monitoring. In: Proceedings of the IEEE 28th Annual Northeast Bioengineering Conference (IEEE Cat. No.02CH37342), pp. 187–188 (2002)
Chunara, R., Andrews, J.R. and Brownstein, J.S.: Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. Am. Soc. Trop. Med. Hygiene 86(1), 39–45 (2012)
Comito, C., Forestiero, A., Pizzuti, C.: Word embedding based clustering to detect topics in social media. In: 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 192–199 (2019)
Comito, C., Falcone, D., Forestiero, A.: Integrating IoT and social media for smart health monitoring. In: Proceedings of the Web Intelligence And Intelligent Agent Technology (2020)
Comito, C., Falcone, D. and Forestiero, A.: A power-aware approach for smart health monitoring and decision support. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1389–1395 (2020)
Culotta, A.: Towards detecting influenza epidemics by analyzing twitter messages. In: Proceedings of the First Workshop on Social Media Analytics, SOMA ’10, pp. 115–122. Association for Computing Machinery, New York (2010)
Denecke, K., Krieck, M., Otrusina, L., Smrz, P., Dolog, P., Nejdl, W., Velasco, E.: How to exploit twitter for public health monitoring? Methods Inf. Med. 52(4), 326–39 (2013)
Deshkar, S., Thanseeh, R.A., Menon, V.G.: A review on IoT based m-health systems for diabetes. Int. J. Comput. Sci. Telecommun. 8(1), 13–18 (2017)
Diaz-Aviles, E., Stewart, A.: Tracking twitter for epidemic intelligence: Case study: EHEC/HUS outbreak in Germany, 2011. In: Proceedings of the 4th Annual ACM Web Science Conference, WebSci ’12, pp. 82–85. Association for Computing Machinery, , New York (2012)
Fan, Y.J., Yin, Y.H., Xu, L., Zeng, Y., Wu, F.: IoT-based smart rehabilitation system. IEEE Trans. Ind. Inf. 10, 1568–1577 (2014)
Faust, O., Hagiwara, Y., Hong, T.J., Lih, O.S., Acharya, U.R.: Deep learning for healthcare applications based on physiological signals: A review. Comput. Methods Prog. Biomed. 161, 1–13 (2018)
Fu, C., Zhang, P., Jiang, J., Yang, K., Lv, Z.: A Bayesian approach for sleep and wake classification based on dynamic time warping method. Multimedia Tools Appl. 76(17), 17765–17784 (2017)
Gomide, J., Veloso, A., Meira Jr, W., Almeida, V., Benevenuto, F., Ferraz, F. and Teixeira, M.: Dengue surveillance based on a computational model of spatio-temporal locality of twitter. In: Proceedings of the 3rd International Web Science Conference, pp. 1–8 (2011)
Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama 316(22), 2402–2410 (2016)
Gunn, A.: The diagnosis of acute abdominal pain with computer analysis. J. R. Coll. Surg. Edinb. 21(3), 170–172 (1976)
Gupta, P., Maharaj, B.T., Malekian, R.: A novel and secure IoT based cloud centric architecture to perform predictive analysis of users activities in sustainable health centres. Multimedia Tools Appl. 76, 18489–18512 (2016)
Hamza, R., Yan, Z., Muhammad, K., Bellavista, P. and Titouna, F.: A privacy-preserving cryptosystem for IoT e-healthcare. Inf. Sci. 527, 493–510 (2020)
Hossain, M.S., Muhammad, G.: Deep learning based pathology detection for smart connected healthcares. IEEE Netw. 34(6), 120–125 (2020)
Hyung, W.J., Son, T., Park, M., Lee, H., Kim, Y.N., Kim, H.I., Kim, J.W., Cheong, J.H., Choi, S.H., Noh, S.H., Kim, J.: Superior prognosis prediction performance of deep learning for gastric cancer compared to Yonsei prognosis prediction model using Cox regression. J. Clin. Oncol. 35(4_suppl), 164–164 (2017)
Kaur, A., Jasuja, A.: Health monitoring based on IoT using raspberry PI. In: 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 1335–1340. IEEE, Piscataway (2017)
Lee, K., Agrawal, A., Choudhary, A.: Real-time disease surveillance using twitter data: Demonstration on flu and cancer. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’13, pp. 1474–1477. Association for Computing Machinery, New York (2013)
Lin, K., Xia, F., Wang, W., Tian, D., Song, J.: System design for big data application in emotion-aware healthcare. IEEE Access 4, 6901–6909 (2016)
Linke, A.C., Mash, L.E., Fong, C.H., Kinnear, M.K., Kohli, J.S., Wilkinson, M., Tung, R., Keehn, R.J., Carper, R.A., Fishman, I., et al.: Dynamic time warping outperforms Pearson correlation in detecting atypical functional connectivity in autism spectrum disorders. NeuroImage 223, 117383 (2020)
Mainetti, L., Manco, L., Patrono, L., Secco, A., Sergi, I., Vergallo, R.: An ambient assisted living system for elderly assistance applications. In: 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1–6 (2016)
Mamoshina, P., Vieira, A., Putin, E., Zhavoronkov, A.: Applications of deep learning in biomedicine. Mol. Pharm. 13(5), 1445–1454 (2016)
Mohammadian, E., Noferesti, M., Jalili, R.: Fast: Fast anonymization of big data streams. In: Proceedings of the 2014 International Conference on Big Data Science and Computing, BigDataScience ’14. Association for Computing Machinery, New York (2014)
Moosavi, S.R., Gia, T.N., Nigussie, E., Rahmani, A.M., Virtanen, S., Tenhunen, H., Isoaho, J.: End-to-end security scheme for mobility enabled healthcare Internet of things. Future Gener. Comput. Syst. 64, 108–124 (2016)
Mutlag, A.A., Khanapi Abd Ghani, M., Mohammed, M.A., Maashi, M.S., Mohd, O., Mostafa, S.A., Abdulkareem, K.H., Marques, G., de la Torre DÃez, I.: Mafc: Multi-agent fog computing model for healthcare critical tasks management. Sensors 20(7), 1853 (2020)
Odlum, M., Yoon, S.: What can we learn about the ebola outbreak from tweets? Am. J. Infect. Control 43(6), 563–571 (2015)
Pathak, S., Kumar, M., Mohan, A. and Kumar, B.: Energy optimization of ZigBee based WBAN for patient monitoring. Procedia Comput. Sci. 70, 414–420 (2015)
Peiffer-Smadja, N., Rawson, T.M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F.X., Birgand, G., Holmes, A.H.: Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin. Microbiol. Infect. 26(5), 584–595 (2020)
Poplin, R., Chang, P.C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P.T., et al.: A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36(10), 983–987 (2018)
Prouski, G., Jafari, M., Zarrabi, H.: Internet of things in eye diseases, introducing a new smart eyeglasses designed for probable dangerous pressure changes in human eyes. In: 2017 International Conference on Computer and Applications (ICCA), pp. 364–368 (2017)
Ramesh, A.N., Kambhampati, C., Monson, J.R.T., Drew, P.J.: Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 86(5), 334 (2004)
Ray, P.P.: Home health hub Internet of things (h3IoT): An architectural framework for monitoring health of elderly people. In: 2014 International Conference on Science Engineering and Management Research (ICSEMR), pp. 1–3 (2014)
Roy, A., Klinefelter, A., Yahya, F.B., Chen, X., Gonzalez-Guerrero, L.P., Lukas, C.J., Kamakshi, D.A., Boley, J., Craig, K., Faisal, M., Oh, S., Roberts, N.E., Shakhsheer, Y., Shrivastava, A., Vasudevan, D.P., Wentzloff, D.D., Calhoun, B.H.: A 6.45 μw self-powered soc with integrated energy-harvesting power management and ULP asymmetric radios for portable biomedical systems. IEEE Trans. Biomed. Circuits Syst. 9(6), 862–874 (2015)
Santos, A., Macedo, J., Costa, A., Nicolau, M.J.: Internet of things and smart objects for m-health monitoring and control. Procedia Technology, 16:1351–1360, 2014. CENTERIS 2014 - Conference on ENTERprise Information Systems/ProjMAN 2014 - International Conference on Project MANagement/HCIST 2014 - International Conference on Health and Social Care Information Systems and Technologies.
Sarma, J., Katiyar, A., Biswas, R., Mondal, H.K.: Power-aware IoT based smart health monitoring using wireless body area network. In: 20th International Symposium on Quality Electronic Design (ISQED), pp. 117–122. IEEE, Piscataway (2019)
Sato, J.A., Yoshida, K.: Wearable ECG monitoring and alerting system associated with smartphone. Int. J. E-Health Med. Commun. 4, 1–15 (2013)
Sedayao, J., Bhardwaj, R., Gorade, N.: Making big data, privacy, and anonymization work together in the enterprise: experiences and issues. In: 2014 IEEE International Congress on Big Data, pp. 601–607. IEEE, Piscataway (2014)
Shaltis, P.A., Reisner, A., Asada, H.H.: Wearable, cuff-less PPG-based blood pressure monitor with novel height sensor. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 908–911 (2006)
Shorten, G., Burke, M.: The application of dynamic time warping to measure the accuracy of ECG compression. Int. J. Circ. Syst. Signal Process. 5(3), 305–313 (2011)
Sidana, S., Amer-Yahia, S., Clausel, M., Rebai, M., Mai, S.T., Amini, M.R.: Health monitoring on social media over time. IEEE Trans. Knowl. Data Eng. 30(8), 1467–1480 (2018)
Sung, W.T., Chang, K.Y.: Evidence-based multi-sensor information fusion for remote health care systems. Sensors Actuators A Phys. 204, 1–19 (2013)
Tomašev, N., Glorot, X., Rae, J.W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al.: A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572(7767), 116–119 (2019)
Turkki, R., Linder, N., Kovanen, P.E., Pellinen, T., Lundin, J.: Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. J. Pathol. Inform. 7(1), 38 (2016)
Tuzcu, V., Nas, S.: Dynamic time warping as a novel tool in pattern recognition of ECG changes in heart rhythm disturbances. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 182–186 (2005)
Uhm, K.E., Yoo, J.S., Chung, S.H., Lee, J.D., Lee, I., Kim, J.I., Lee, S.K., Nam, S.J., Park, Y.H., Lee, J.Y. and Hwang, J.H.: Effects of exercise intervention in breast cancer patients: is mobile health (mhealth) with pedometer more effective than conventional program using brochure? Breast Cancer Res. Treat. 161(3), 443–452 (2017)
Vermesan, O., Friess, P., Guillemin, P., Gusmeroli, S., Sundmaeker, H., Bassi, A., Jubert, I.S., Mazura, M., Harrison, M., Eisenhauer, M., et al.: Internet of things strategic research roadmap. Internet Things Glob. Technol. Societal Trends 1(2011), 9–52 (2011)
Villarrubia, G., Bajo, J., De Paz, J.F., Corchado, J.M.: Monitoring and detection platform to prevent anomalous situations in home care. Sensors 14(6), 9900–9921 (2014)
Woznowski, P., Fafoutis, X., Song, T., Hannuna, S., Camplani, M., Tao, L., Paiement, A., Mellios, E., Haghighi, M., Zhu, N., et al.: A multi-modal sensor infrastructure for healthcare in a residential environment. In: 2015 IEEE International Conference on Communication Workshop (ICCW), pp. 271–277. IEEE, Piscataway (2015)
Yin, H., Jha, N.K.: A health decision support system for disease diagnosis based on wearable medical sensors and machine learning ensembles. IEEE Trans. Multi-Scale Comput. Syst. 3(4), 228–241 (2017)
Yin, H., Zhu, X., Ma, S., Yang, S., Chen, L.: A novel similarity comparison approach for dynamic ECG series. Bio-med. Mater. Eng. 26(Suppl 1), S1095–105 (2015)
Zhang, X., Yang, L.T., Liu, C., Chen, J.: A scalable two-phase top-down specialization approach for data anonymization using mapreduce on cloud. IEEE Trans. Parallel Distrib. Syst. 25(2), 363–373 (2013)
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Comito, C., Falcone, D., Forestiero, A. (2022). Convergence Between IoT and AI for Smart Health and Predictive Medicine. In: Comito, C., Forestiero, A., Zumpano, E. (eds) Integrating Artificial Intelligence and IoT for Advanced Health Informatics. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-91181-2_5
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