Efficient Low Power Intelligent Health Care Monitoring System using IoT
Revu Smile1, N. M. Ramalingeswara Rao2
1Revu Smile, Electronics and Communication Engineering , Godavari inst. Of Engineering and Technology, Rajahmundry, India.
2N.M.Ramalingeswara Rao, Electronics and Communication Engineering, Godavari inst. Of Engineering and Technology, Rajahmundry, India.
Manuscript received on September 12, 2019. | Revised Manuscript received on 22 September, 2019. | Manuscript published on October 10, 2019. | PP: 1707-1711 | Volume-8 Issue-12, October 2019. | Retrieval Number: L31801081219/2019©BEIESP | DOI: 10.35940/ijitee.L3180.1081219
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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: The present paper presents wellbeing checking framework for patients. In every one of the cases existences of individual behind the patient in current days are hard to screen, even in the medicinal services focus it might have the shot of possibility to happen uneven things in basic conditions. Keeping in mind the end goal to evade this issue this frame work will fare the well and effectively exchange data of patient to relatives, as well even the patient was not in the premises of healing facility. Data of the patient can be sent to relatives and alarms them by sending message. By utilizing this framework we can get GPS information of the patient, thereof we can rapidly get involves in basic circumstances. We can locate the patient fall discovery by the status of patient and can have an eye without being there with persistent. The major factor that play important role in wearables is power consumption, the least power drowning model has been developed in this system. And the controller runs Real Time Operating System (RTOS) to execute tasks almost at the same time. The cloud service used here is the amazon IoT, and there is a chance to analyze the data with machine learning models to predict the future abnormal situations.
Keywords: Accelerometer, ECG signal acquisition, Fall detection, GPS, GSM-GPRS, IoT, Machine learning, Power modes, RTOS .
Scope of the ArticleMachine learning