An analytical review for cloud computing in healthcare system based biosensors
Keywords:
Healthcare, Cloud Computing, Biosensors, Time Condition Monitoring, Real Time, Patients Case RecognitionAbstract
For patients with geriatric medical specialty disorders equivalent to dementia, patients with ingrained devices such as pacemakers. The quantity of exercise and therefore the amount of daylight are important guides for dosing and treatment, thus observation daily health info is important to patient safety and health. A portable, wearable device and server configuration to watch information are required to supply these services to patients. The true period of time condition monitoring systems should embody GPS, accelerometer, and lightweight sensor, and might acquire health information in real-time by measure position for patients, the amount of exercise, the amount of sunlight, heart rate, blood pressure, the amount of atomic number within the blood, and different devices are often supplemental in step with the wants of the patient's condition. The server system includes sensor information, analysis rule and an internet server that's employed by the practitioner and the guardian to watch the sensor data noninheritable from the good sensors.
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Abudayyeh OO, Gootenberg JS, Konermann S, Joung J, Slaymaker IM, Cox DB,… Severinov K (2016) C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector. Science 353(6299):aaf5573.
Acampora G., Cook D. J., Rashidi P., and Vasilakos AV. (2013) A Survey on Ambient Intelligence in Healthcare. Proceedings of the IEEE, 101(12):2470-2494,
Aileni Aileni RM, Pasca S, Valderrama C (2015) Cloud computing for big data from biomedical sensors monitoring, storage and analyze. In: 2015 conference grid, Cloud & High Performance Computing in science (ROLCG) (pp. 1-4). IEEE.
An, X.; Stylios, G.A (2018) Hybrid textile electrode for electrocardiogram (ECG) Measurement and Motion Tracking. Materials, 11, 1887.
Ankhili, A.; Tao, X.; Cochrane, C.; Coulon, D.; Koncar, V. (2018) Washable and Reliable Textile Electrodes Embedded into Underwear Fabric for Electrocardiography (ECG) Monitoring. Materials, 11, 256.
Avery P. IT Business Edge. 2009 Aug 26. Research Indicates Increase in Cloud Computing
Bahga A. and Madisetti V. K. (2013) A Cloud-based Approach for Interoperable Electronic Health Records (EHRs), IEEE J. Biomed. Health Inform., 17(5), 894–906.
Bannerman PL. Cloud Computing Adoption Risks: State of Play. In: Proceedings of the 17th Asia Pacific Software Engineering Conference Cloud Workshop. New York, NY: IEEE; 2010:10-16.
Bellazzi R (2014) Big data and biomedical informatics: a challenging opportunity. Yearb. Med. Inform. 23(01):08–13.
Benharref A. and Serhani M. A. (2014) Novel cloud and SOA-based framework for E-health monitoring using wireless biosensors,” IEEE J. Biomed. Health Inform., 18(1), pp. 46–55.
Berntson, G.G.; Thomas Bigger, J.; Eckberg, D.L.; Grossman, P.; Kaufmann, P.G.; Malik, M.; Nagaraja, H.N.; Porges, S.W.; Saul, J.P.; Stone, P.H.; et al. (1997) Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology, 34, 623–648
Boano, C.A.; Lasagni, M.; Romer, K.; Lange, T. (2011) Accurate temperature measurements for medical research using body sensor networks. In Proceedings of the 2011 14th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops, Newport Beach, CA, USA; pp. 189–198.
Brey P (2007) Ethical aspects of information security and privacy. In: security, privacy, and trust in modern data management. Springer, Berlin, Heidelberg, pp 21–36.
Brownlee J (2016) Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end. Machine Learning Mastery
Burtovaya, N. B. (2020). Teenagers’ maladjustment problem. International Journal of Social Sciences and Humanities, 4(2), 21–29. https://doi.org/10.29332/ijssh.v4n2.402
Castaneda, D.; Esparza, A.; Ghamari, M.; Soltanpur, C.; Nazeran, H. (2018) A review on wearable photoplethysmography sensors and their potential future applications in health care. Int. J. Biosens. Bioelectron., 4, 195–202.
Caygill, RL.; Blair, GE. & Millner, PA. (2010). A review on viral biosensors to detect human pathogens. Analytica Chimica Acta, Vol.681, No. 12, pp. 8-15.
Chan M., Campo E., and Fourniols J. (2009) Smart homes current features and future perspectives. Maturitas, 64(2):90-97.
Chang EY, Wu MH, Tang KF, Kao HC, Chou, CN (2017). Artificial intelligence in XPRIZE DeepQ Tricorder. In MMHealth@ MM (pp. 11-18)
Chatman C. How cloud computing is changing the face of health care information technology. J Health Care Compliance 2010;12(3):37-70.
Cherry S. Forecast for cloud computing: up, up, and away. IEEE Spectrum 2009;46(10):68.
Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, … Xie W (2018) Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 15(141): 20170387
Clark Jr., LC. & Lyons, C. (1962). Electrode systems for continuous monitoring in cardiovascular surgery. Annals of the New York Academy of Sciences, Vol.102, No.1, pp. 29-45.
Danek J. Public Works Government Services Canada. 2009 Oct 6. Cloud Computing and the Canadian Environment
Das A, Pradhapan P, Groenendaal W, Adiraju P, Rajan RT, Catthoor F, … Van Hoof C (2018) Unsupervised heart-rate estimation in wearables with liquid states and a probabilistic readout. Neural Netw 99:134–147.
Das, P.S.; Kim, J.W.; Park, J.Y. (2019) Fashionable wrist band using highly conductive fabric for electrocardiogram signal monitoring. J. Ind. Text., 49, 243–261.
Deen, M.J. Information and communications technologies for elderly ubiquitous healthcare in a smart home. Pers. Ubiquit. Comput. 2015, 19, 573–599.
Demiris G., Rantz M. J., Aud M., Marek K., Tyrer H., Skubic M., and Hussam A. (2004) Older adults' attitudes towards and perceptions of'smart home'technologies: a pilot study. Informatics for Health and Social Care, 29(2):87-94.
Ding, L.; Du, D.; Zhang, X. & Ju, H. (2008). Trends in cell-based electrochemical biosensors. Current Medicinal Chemistry, Vol.15, No.30, pp. 3160-3170.
D'Orazio, P. (2003). Biosensors in clinical chemistry. Clinica Chimica Acta, Vol.334, No. 1-2, pp. 41-69
Dowrick A. and Southern. Dementia 2014: Opportunityfor change. Alzheimer's Society publications, 2014.
Dudley JT, Pouliot Y, Chen R, Morgan AA, Butte AJ. Translational bioinformatics in the cloud: an affordable alternative. Genome Med 2010;2(8):51
Elgendi, M.; Fletcher, R.; Liang, Y.; Howard, N.; Lovell, N.H.; Abbott, D.; Lim, K.; Ward, R. (2019) The use of photoplethysmography for assessing hypertension. NPJ Digit. Med., 2, 60.
Elliott, M., Coventry, A. (2012) Critical care: The eight vital signs of patient monitoring. Br. J. Nurs., 21, 621–625.
Geneva, I.; Cuzzo, B.; Fazili, T.; Javaid, W. (2019) Normal body temperature: A systematic review. Open forum Infectious Dis., 6.
Gilaberte, S.; Gómez-Clapers, J.; Casanella, R.; Pallas-Areny, R. (2010) Heart and respiratory rate detection on a bathroom scale based on the ballistocardiogram and the continuous wavelet transform. In Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina; pp. 2557–2560.
Gul O., Al-Qutayri M., Yeun C. Y., and Vu Q. H. (2012) Framework of a national level electronic health record system. In 2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM), pp. 60–65.
Ha T, Tran J, Liu S, Jang H, Jeong H, Mitbander R, Huh H, Qiu Y, Duong J, Wang R.L, et al. (2019) A chest-laminated ultrathin and stretchable E-tattoo for the measurement of electrocardiogram, seismocardiogram, and cardiac time intervals. Adv. Sci., 6, 1900290.
Haughton J. Year of the underdog: Cloud-based EHRs. Health Manag Technol 2011;32(1):9.
Hendrick E., Schooley B., and Gao C. (2013) Cloud Health: Developing a reliable cloud platform for healthcare applications. IEEE 10th Consumer Communications and Networking Conference, pp. 887–891.
Huang H, Su S, Wu N, Wan H, Wan S, Bi H and Sun L, Graphene-Based Sensors for Human Health Monitoring. Front. Chem. 7:399, 2019. doi: 10.3389/fchem.2019.00399
Huang, R.; Lin, Y.; Shi, Q.; Flowers, L.; Ramachandran, S.; Horowitz, IR.; et al. (2004). Enhanced protein profiling arrays with ELISA-based amplification for high throughput molecular changes of tumor patients' plasma. Clinical Cancer Research, Vol.10, No.2, pp. 598-609
Jatmiko W, Arsa DMS, Wisesa H, Jati G, Ma’Sum MA (2016) A review of big data analytics in the biomedical field. In: 2016 international workshop on big data and information security (IWBIS) (pp. 31- 41). IEEE.
Jones VM, in’t Veld RH, Tonis T, Bults RB, Van Beijnum B, Widya I, Hermens H (2008) Biosignal and context monitoring: distributed multimedia applications of body area networks in healthcare. In: 2008, IEEE 10th Workshop on Multimedia Signal Processing (pp. 820–825). IEEE
Kabachinski J. What's the forecast for cloud computing in healthcare? Biomed Instrum Technol 2011;45(2):146-150.
Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I (2017) Machine learning and data mining methods in diabetes research. Comput. Struct. Biotechnol. J. 15:104–116.
Kim DH; Lu N; Ma R; Kim YS; Kim RH; Wang S; Wu J; Won SM; Tao H; Islam A; et al. (2011) Epidermal Electronics. Science, 333, 838–843.
Kranjec, J.; Beguš, S.; Geršak, G.; Šinkovec, M.; Drnovšek, J.; Hudoklin, D. (2017) Design and Clinical Evaluation of a Non-Contact Heart Rate Variability Measuring Device. Sensors, 17, 2637.
Looney, D.P.; Buller, M.J.; Gribok, A.V.; Leger, J.L.; Potter, A.W.; Rumpler,W.V.; Tharion,W.J.;Welles, A.P.; Friedl, K.E.; Hoyt, R.W. (2018) Estimating resting core temperature using heart rate. J. Meas. Phys. Behav., 1, 79–86.
Lu, G.; Yang, F.; Taylor, J.A.; Stein, J.F. (2009) A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects. J. Med. Eng. Technol., 33, 634–641.
Majumder S., Mondal T., Deen, M. (2017) Wearable Sensors for Remote Health Monitoring. Sensors, 17, 130.
Malhi, K.; Mukhopadhyay, S.C.; Schnepper, J.; Haefke, M.; Ewald, H. (2012) A Zigbee-Based Wearable Physiological Parameters Monitoring System. IEEE Sens. J., 12, 423–430.
Malik, M. (1996) Heart rate variability: Standards of measurement, physiological interpretation, and clinical use: Task force of the European Society of Cardiology and the North American Society for Pacing and Electrophysiology. Circulation, 93, 1043–1065.
Marling C, Xia L, Bunescu R, Schwartz F (2016) Machine learning experiments with noninvasive sensors for hypoglycemia detection. In: proceedings of IJCAI workshop on knowledge discovery in healthcare data. Morgan Kaufmann publishers Inc., San Francisco, pp 1–6.
Mary X.A., Mohan S., Evangeline S., Rajasekaran K. (2020) Physiological parameter measurement using wearable sensors and cloud computing. In Systems Simulation and Modeling for Cloud Computing and Big Data Applications; Amsterdam, The Netherlands, pp. 15–27.
Meadows, D. (1996). Recent developments with biosensing technology and applications in the pharmaceutical industry. Advanced Drug Delivery Reviews, Vol.21, No.3, pp. 179-189.
Miskelly F. G. (2001) Assistive technology in elderly care. Age and ageing, 30(6):455-458.
Mohammed S., Servos D., and Fiaidhi J. (2011) Developing a secure distributed OSGi cloud computing infrastructure for sharing health records in autonomous and intelligent systems. Second International Conference, AIS 2011, pp. 241–52.
Mohanty S, Jagadeesh M, Srivatsa H (2013) Big data imperatives: Enterprise ‘Big Data’warehouse,‘BI’implementations and analytics. Apress
Munir, K.; Elahi, H.; Ayub, A.; Frezza, F.; Rizzi, A. Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers 2019, 11, 1235.
Nehal SA, Roy D, Devi M, Srinivas T (2019) Highly sensitive lab-on-chip with deep learning AI for detection of bacteria in water. Int. J. Inf. Technol.:1–7.
Paiva JS, Cardoso J, Pereira T (2018) Supervised learning methods for pathological arterial pulse wave differentiation: a SVM and neural networks approach. Int J Med Inform 109:30–38
Palecek, E. (2002). Past, present and future of nucleic acids electrochemistry. Talanta, Vol. 56, No.5, pp. 809-819.
Patra M.R., Das R.K., Padhy R.P. (2012) CRHIS: Cloud based rural healthcare information system. In Proceedings of the 6th International Conference on Theory and Practice of Electronic Governance, New York, NY, USA, 402–405.
Pereira T.; Tran N.; Gadhoumi K.; Pelter M.M.; Do D.H.; Lee R.J.; Colorado R.; Meisel K.; Hu X. (2020) Photoplethysmography based atrial fibrillation detection: A review. NPJ Digit. Med., 3, 3.
Pinheiro N.; Couceiro R.; Henriques J.; Muehlste J.; Quintal I.; Goncalves L.; Carvalho P. (2016) Can PPG be used for HRV analysis? In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20; 2945–2949.
Pino E.J.; Chavez J.A.P.; Aqueveque P. (2017) BCG algorithm for unobtrusive heart rate monitoring. In Proceedings of the 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), Bethesda, MD, USA, 6–8; 180–183.
Pino E.J.; Larsen C.; Chavez J.; Aqueveque P. (2016) Non-invasive BCG monitoring for non-traditional settings. In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20; 4776–4779.
Postolache O.; Girão P.S.; Postolache G. (2013) Pervasive sensing and m-health: vital signs and daily activity monitoring in pervasive and mobile sensing and computing for healthcare; Mukhopadhyay, S.C., Postolache, O.A., Eds.; Springer: Berlin/Heidelberg, Germany; 2, pp. 1–49.
Postolache O.; Girao P.S.; Postolache G.; Gabriel J. (2011) Cardio-respiratory and daily activity monitor based on FMCW Doppler radar embedded in a wheelchair. In Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30; pp. 1917–1920.
Postolache O.A.; Girao P.M.B.S.; Mendes J.; Pinheiro E.C.; Postolache G. (2010) Physiological Parameters Measurement Based on Wheelchair Embedded Sensors and Advanced Signal Processing. IEEE Trans. Instrum. Meas., 59, 2564–2574.
Pramanik P.K.D.; Upadhyaya B.K.; PalS.; Pal T. (2019) Internet of things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare. In Healthcare Data Analytics and Management; Elsevier: Amsterdam, The Netherlands, pp. 1–58.
Presti D.L.; Massaroni C.; Di-Tocco J.; Schena E.; Formica D.; Caponero M.A.; Longo U.G.; Carnevale A.; D’Abbraccio J.; Massari L.; et al. (2019) Cardiac monitoring with a smart textile based on polymer-encapsulated FBG: Influence of sensor positioning. In Proceedings of the 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Istanbul, Turkey, 26–28; pp. 1–6.
Quan TM, Doike T, Bui DC, Arata S, Kobayashi A, Islam MZ, Niitsu K (2019) AI-based edge-intelligent hypoglycemia prediction system using alternate learning and inference method for blood glucose level data with low-periodicity. In: 2019 IEEE international conference on artificial intelligence circuits and systems (AICAS) (pp. 201-206). IEEE.
Rajendra AU.; Paul Joseph, K.; Kannathal, N.; Lim, C.M.; Suri, J.S. (2006) Heart rate variability: A review. Med. Biol. Eng. Comput., 44, 1031–1051.
Robles RJ, Kim T. Applications, Systems and Methods in Smart Home Technology: A Review. International Journal of Advanced Science and Technology Vol. 15, February, 2010. pp 37-47
Rolim C. O., Koch F. L., Westphall C. B., Werner J., Fracalossi A., and Salvador G. S. (2010) A cloud computing solution for patient’s data collection in health care institutions. Second International Conference on eHealth, Telemedicine, and Social Medicine (ETELEMED), 2010, pp. 95–99.
Rosenthal A, Mork P, Li MH, Stanford J, Koester D, Reynolds P. Cloud computing: a new business paradigm for biomedical information sharing. J Biomed Inform 2010;43(2):342-353.
Sadri F. (2011) Ambient intelligence. ACM Computing Surveys, 43(4):1-66.
Schweitzer EJ. (2011) Reconciliation of the cloud computing model with US federal electronic health record regulations. J Am Med Inform Assoc.
Sheth RU, Wang HH (2018) DNA-based memory devices for recording cellular events. Nat. Rev. Genet. 19(11):718–732.
Soroudi, A.; Hernández, N.;Wipenmyr, J.; Nierstrasz, V. (2019) Surface modification of textile electrodes to improve electrocardiography signals in wearable smart garment. J. Mater. Sci. Mater. Electron., 30, 16666–16675.
Spichiger-Keller, UE. (1998). Chemical sensors and biosensors for medical and biological applications. Weinheim: Wiley-VCH, ISBN 978-352-7612-28-4, Verlag, GmbH.
Sugimoto, C.; Kohno, R. (2011) Wireless sensing system for healthcare monitoring thermal physiological state and recognizing behavior. In Proceedings of the 2011 International Conference on Broadband and Wireless Computing, Communication and Applications, Barcelona, Spain; pp. 285–291.
Suryasa, I. W., Rodríguez-Gámez, M., & Koldoris, T. (2022). Post-pandemic health and its sustainability: Educational situation. International Journal of Health Sciences, 6(1), i-v. https://doi.org/10.53730/ijhs.v6n1.5949
Susilo, C. B., Jayanto, I., & Kusumawaty, I. (2021). Understanding digital technology trends in healthcare and preventive strategy. International Journal of Health & Medical Sciences, 4(3), 347-354. https://doi.org/10.31295/ijhms.v4n3.1769
Toga AW, Foster I, Kesselman C, Madduri R, Chard K, Deutsch EW, … Ames J (2015) Big biomedical data as the key resource for discovery science. J Am Med Inform Assoc 22(6):1126–1131.
Tsukada, Y.T.; Tokita, M.; Murata, H.; Hirasawa, Y.; Yodogawa, K.; Iwasaki, Y.; Asai, K.; Shimizu,W.; Kasai, N.; Nakashima, H.; et al. (2019) Validation of wearable textile electrodes for ECG monitoring. Heart Vessels, 34, 1203–1211.
Van-Gorp P. and Comuzzi M. (2014) Lifelong personal health data and application software via virtual machines in the cloud. IEEE J. Biomed. Health Inform., 18(1), 36–45.
Vilaplana J., Solsona F., Abella F., Filgueira R., and Rius J. (2013) The cloud paradigm applied to e- Health, BMC Med. Inform. Decis. Mak., 13(1), 1-35.
Wah TY, Gopal Raj R, Iqbal U (2018) Automated diagnosis of coronary artery disease: a review and workflow Cardiology research and practice, vol 2018, pp 2018–2019.
Wanekaya AK., Chen W., Mulchandani, A. (2008). Recent biosensing developments in environmental security. Journal of Environmental Monitoring, 10(6), pp. 703-712.
Webb, R.C.; Ma, Y.; Krishnan, S.; Li, Y.; Yoon, S.; Guo, X.; Feng, X.; Shi, Y.; Seidel, M.; Cho, N.H.; et al. Epidermal devices for noninvasive, precise, and continuous mapping of macrovascular and microvascular blood flow. Sci. Adv. 2015, 1, e1500701.
Wiles J. L. and Jayasinha R. Care for place: The contributions older people make to their communities. Journal of aging studies, 27(2):93-101, 2013.
Wimo A., Winblad B., and Jonsson L.. An estimate of the total worldwide societal costs of dementia in 2005. Alzheimer's & Dementia, 3(2):81-91, 2007.
Wooten R., Klink R., Sinek F., Bai Y., and Sharma M. (2012) Design and implementation of a secure healthcare social cloud system. In 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 805–810.
Wu R, Ahn GJ, Hu H. Secure sharing of electronic health records in clouds. 2012 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2012), 2012, pp. 711–18.
Xu, S.; Zhang, Y.; Jia, L.; Mathewson, K.E.; Jang, K.-I.; Kim, J.; Fu, H.; Huang, X.; Chava, P.;Wang, R.; et al. (2014) Soft Microfluidic Assemblies of Sensors, Circuits, and Radios for the Skin. Science, 344, 70–74.
Zhou, Y.; Ding, X.; Zhang, J.; Duan, Y.; Hu, J.; Yang, X. (2014) Fabrication of conductive fabric as textile electrode for ECG monitoring. Fibers Polym., 15, 2260–2264.
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