Context Aware Quick Sensor Service (CQSS) to Remote Patients
Sushama Rani Dutta1, Sujoy Datta2, Monideepa Roy3
1Sushama Rani Dutta, SRF in ITRA project in the School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar
2Sujoy Datta, Assistant Professor in the School of Computer Engineering, KIIT Deemed University, Bhubaneswar
3Monideepa Roy, Associate Professor at KIIT Deemed University, Bhubaneswar.
Manuscript received on 24 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 768-774 | Volume-8 Issue-9, July 2019 | Retrieval Number: H6822068819/19©BEIESP | DOI: 10.35940/ijitee.H6822.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Using sensors in healthcare can greatly improve the quality of life, especially for elderly patients. The data from the sensors of the patients is constantly monitored for abnormalities at a server. Whenever this data crosses a threshold value, the information is notified to the corresponding doctor. The doctor can then take the necessary action. However an inspection of historical data has shown that some conditions of patients have cyclic patterns and the medications are often repeated. The proposed system is designed to assist the doctor in diagnosis by retrieving those patterns. We have compared the times taken for receiving responses from the two different systems and a significant amount of improvement was noticed. We have introduced a Dynamic Context Aware Technique (DCAT) which can improve the quality of 24 hour monitoring patient. This paper presents the design and implementation of a system based on DCAT using SAMSUNG GEAR S (Heart rate monitor sensor.The backend remote centralized computation and data storage can decreases the workload of the remote health care provider by avoiding of sending the identical and similar cases data to the doctors. This improves the processing speed and also gives solutions in case of the unavailability of doctors in some cases. Experimental results based on real datasets show that our system is highly efficient and scalable to a long time monitoring patients.
Index Terms: Context Aware; Machine Learning Technique; Continuous Monitoring; DCAT; CQSS Middleware
Scope of the Article: Machine Learning