A Customized Knowledge Discovery Framework for Assisted Healthcare Employing

SEEE DIGIBOOK ON ENGINEERING & TECHNOLOGY, VOL. 01, DEC 2019 PP.(307-313)
Abstract– An ambient assisted living system, also known as an AAL system, is made up of a variety of sensors and other devices, all of which produce massive amounts of patient-specific unstructured raw data on a daily basis. The ability of remote monitoring applications to accurately identify abnormal conditions exhibited by a patient and, as a result, send appropriate alerts to the individuals responsible for the patient’s care is an important feature of these applications. In conventional medical practice, situations are categorized according to overly generalized medical rules or nebulous guidelines, neither of which are always appropriate for every type of patient. These systems are not capable of early-stage foresight into the future. If a patient is feeling unwell, they may be required to press a panic button that is attached to their clothing in order to alert a response center that an emergency has occurred. In this body of work, we have presented BDCaM (Big Data – Context Aware Monitoring), a generalized framework for personalized healthcare that capitalizes on the benefits of context-aware computing, remote monitoring, cloud computing, machine learning, and big data. BDCaM was designed to provide patients with more accurate and timely diagnoses and treatment plans. Our solution offers a methodical strategy for providing assistance to the rapidly expanding communities of people with chronic illnesses who live alone and have a requirement for assisted care. The system is able to accurately differentiate between emergency situations and normal conditions. The data that are used to validate the model are obtained through the process of artificial data generation, which is based on data derived from actual patients. This method ensures that the correlation of a patient’s vital signs with various activities and symptoms is maintained.
Index Terms – Healthcare Assistance; Context-Aware Data Mining, Hadoop; Knowledge Discovery; MapReduce; big data.
REFERENCE

[1] A. Pantelopoulos and N. Bourbakis, “A survey on wearablesensor-based systems for health monitoring and prognosis,”IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 40, no. 1, pp. 1–12, 2010.
[2] D. N. Monekosso and P. Remagnino, “Behavior analysis for assisted living,” IEEE Transactions on Automation Science and Engineering, vol. 7, no. 4, pp. 879–886, 2010.
[3] P. Groves, B. Kayyali, D. Knott, and S. Van Kuiken, “The bigdata revolution in healthcare,” McKinsey & Company, 2013.
[4] S. Pandey, W. Voorsluys, S. Niu, A. Khandoker, and R. Buyya,“An autonomic cloud environment for hosting ecg data analysis services,” Future Generation Computer Systems, vol. 28, no. 1,pp. 147–154, 2012
[5]A. Ibaida, D. Al-Shammary, and I. Khalil, “Cloud enabled fractal based ecg compression in wireless body sensor networks,”Future Generation Computer Systems, vol. 35, pp. 91–101, 2014.
[6] Australian bureau of statistics – 4821.0.55.001 – cardiovasculardisease in Australia: A snapshot, 2004-05. [Online].
[7] [7] A. Forkan, I. Khalil, and Z. Tari, “Cocamaal: A cloud-oriented context-aware middleware in ambient assisted living,” FutureGeneration Computer Systems, vol. 35, pp. 114–127, 2014.
[8] A. K. Dey, “Providing architectural support for buildingcontext-aware applications,” Ph.D. dissertation, Georgia Institute of Technology, 2000.
[9] S.Sridevi, B. Sayantani, K.P.al Amutha, C. Madan Mohan, R.Pitchiah, “Context Aware Health Monitoring System, ICMB 2010, Springer LNCS 6165, pp. 249-257.
[10] S. B. Siewert. (2013, July) Big data in the cloud. [Online]. Available: http://www.ibm.com/ developerworks/library/bdbigdatacloud/bd- bigdatacloud-pdf.pdf
[11] J. J. Oresko, Z. Jin, J. Cheng, S. Huang, Y. Sun, H. Duschl, and A. C. Cheng, “A wearable smartphone-based platform for realtime cardiovascular disease detection via lectrocardiogramprocessing,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 3, pp. 734–740, 2010.

Sreenivasalu Manda V Bharathi S, Dhanabal S
Department of Information Technology,
Rathinam Technical Campus,
Coimbatore, India

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top