Asthma Patients' Cloud-Based Health Tracking and Monitoring System in Designed Flashpoint

Authors

  • Sandesh Achar Staff Engineer, Intuit Inc., Mountain View, California, USA

DOI:

https://doi.org/10.18034/mjmbr.v4i2.648

Keywords:

Asthma Patients, cloud-based monitoring system, cloud-based health tracking, smart inhaler

Abstract

Asthma is a chronic illness that causes improper respiratory organ function and breathing problems. Three hundred fifty million people worldwide have bronchial asthma, or one in 12 adults. Self-monitoring is the first step in managing chronic illness. This lets doctors and people monitor and address health conditions in real-time. Telemonitoring is a phrase used in IT to remotely monitor the health of patients who are not in hospitals or medical centers. Wearable medical sensors, such as IoT-based remote asthma and blood pressure sensors, capture real-time information from remotely located patients. The medical information is then transmitted through the Internet for medical diagnosis and therapy. Classical Spirometry measures how effectively a patient's lungs function and requires supervision. We want to support impacted patients; thus, we built a monitoring system. With sensors including heartbeat, dust, temperature, and humidity, the device will collect health-related data and upload it to the cloud, helping doctors diagnose patients. This study uses private cloud computing to track and monitor real-time medical information in approved areas. In addition, the private cloud-based environment called a bounded telemonitoring system is meant to capture real-time medical details of patients in the medical centers inside and outside medical wards. In addition, a new wireless sensor network scenario is intended to monitor patients' health information 24/7. This research secures medical information access and guides future medical system development.

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References

Achar, S. (2015). Requirement of Cloud Analytics and Distributed Cloud Computing: An Initial Overview. International Journal of Reciprocal Symmetry and Physical Sciences, 2, 12–18. https://upright.pub/index.php/ijrsps/article/view/70

Achar, S. (2016). Software as a Service (SaaS) as Cloud Computing: Security and Risk vs. Technological Complexity. Engineering International, 4(2), 79-88. https://doi.org/10.18034/ei.v4i2.633 DOI: https://doi.org/10.18034/ei.v4i2.633

Alemdar, H. and Ersoy, C. (2010). Wireless sensor networks for healthcare: a survey. Computer Networks, 54(15), 2688–2710. DOI: https://doi.org/10.1016/j.comnet.2010.05.003

Heilig, L., and Voss, S. (2014). A scientometric analysis of cloud computing literature. IEEE Transactions on Cloud Computing, 2(3), 266–278. DOI: https://doi.org/10.1109/TCC.2014.2321168

Kiotseridis, H., Bjermer, L., Pilman, E., Ställberg, B., Romberg, K., & Tunsäter, A. (2012). ALMA, a new tool for the management of asthma patients in clinical practice: Development, validation and initial clinical findings. Primary Care Respiratory Journal: Journal of the General Practice Airways Group, 21(2), 139-144. https://doi.org/10.4104/pcrj.2011.00091 DOI: https://doi.org/10.4104/pcrj.2011.00091

Ko, J. G., Chenyang, L., Srivastava, M. B., Stankovic, J. A., Terzis, A., and Welsh, M. (2010). Wireless sensor networks for healthcare. Proceedings of the IEEE, 98(11), 1947–1960. DOI: https://doi.org/10.1109/JPROC.2010.2065210

Milenković, A. and Otto, C. (2006). Wireless sensor networks for personal health monitoring: issues and an implementation. Computer Communications, 29(13-14), 2521–2533. DOI: https://doi.org/10.1016/j.comcom.2006.02.011

Prieto, L., Badiola, C., Villa, J. R., Plaza, V., Molina, J., & Cimas, E. (2007). Asthma control: Do patients' and physicians' opinions fit in with patients' asthma control status? The Journal of Asthma: Official Journal of the Association for the Care of Asthma, 44(6), 461-467. DOI: https://doi.org/10.1080/02770900701421989

Reddy, B. E., Kumar, T. V. S. and Ramu, G. (2012). An efficient cloud framework for healthcare monitoring system. In 2012 International Symposium on Cloud & Services Computing, 113–117, Mangalore, India. DOI: https://doi.org/10.1109/ISCOS.2012.11

Saito, N., Itoga, M., Tamaki, M., Yamamoto, A., & Kayaba, H. (2015). Cough variant asthma-patients are more depressed and anxious than classic asthma patients. Journal of Psychosomatic Research, 79(1), 18-26. https://doi.org/10.1016/j.jpsychores.2015.03.011 DOI: https://doi.org/10.1016/j.jpsychores.2015.03.011

Sullivan, S. D., Wenzel, S. E., Bresnahan, B. W., Zheng, B., Lee, J. H., Pritchard, M., . . . Weiss, S. T. (2007). Original article: Association of control and risk of severe asthma related events in severe or difficult-to-treat asthma patients. Allergy, 62(6), 655-660. https://doi.org/10.1111/j.1398-9995.2007.01383.x DOI: https://doi.org/10.1111/j.1398-9995.2007.01383.x

Wen, C., Yeh, M.-F., Chang, K.-C., and Lee, R.-G. (2008). Realtime ECG telemonitoring system design with mobile phone platform. Measurement, 41(4), 463–470. DOI: https://doi.org/10.1016/j.measurement.2006.12.006

Xiong, N., Vasilakos, A. V., Yang, L. T., et al. (2009). Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems. IEEE Journal on Selected Areas in Communications, 27(4), 495–509. DOI: https://doi.org/10.1109/JSAC.2009.090512

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Published

2017-12-31

Issue

Section

Peer-reviewed Article

How to Cite

Achar, S. (2017). Asthma Patients’ Cloud-Based Health Tracking and Monitoring System in Designed Flashpoint. Malaysian Journal of Medical and Biological Research, 4(2), 159-166. https://doi.org/10.18034/mjmbr.v4i2.648