Loading

An Efficient Body Line Health Monitoring System with Alerts Triggered Through Predictive Data Analytics
Jasti Sowmya Sree1, Mohammed Ali Hussain2

1Jasti Sowmya Sree, M.Tech Student, ECM, KL deemed to be University, Vijayawada (Andhra Pradesh), India.
2Dr. Mohammed Ali Hussain, Professor, ECM, KL deemed to be University, Vijayawada (Andhra Pradesh), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1544-1547 | Volume-8 Issue-6, April 2019 | Retrieval Number: F4053048619/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: In actuality, there is a need to monitor the patient continually. Till now there is no programmed cautioning framework has been executed. In this task, we screen heart rate, temperature and produce ECG signal with the assistance of sensor. A sensor sense and record the esteem and sends those to android telephone of the aide or relative to a patient if there is any occurrence of crisis. And furthermore if any traffic is overwhelming the crisis vehicle will redirect to another course by utilizing Google maps. Also, we present a model of an essential social insurance observing framework, which cautions, continuously to the patient and patient’s relative about the encountered issue and corresponding medicinal consideration or hospitalization. The proposed system can reduces the time, easy to utilize. It is also used for self monitoring the patients anywhere at any time. In this way by using this Smart health observing framework reduces the effort of experts and paramedical staff to screen the patient for 24 hours and moreover decreases the time and cost.
Keyword: Internet of Things, Raspberry Pi, Health Monitoring, Temperature Sensor, ECG Sensor.
Scope of the Article: Health Monitoring and Life Prediction of Structures