Fall Detection System for Monitoring Elderly People
S. Hitesh Kumar1, R. Kavya Reddy2, Preetha K.S3
1S. Hitesh Kumar, Department of Electronics Engineering, Vellore Institute of Technology, VIT University, Vellore (Tamil Nadu). India.
2R. Kavya Reddy, Department of Electronics Engineering, Vellore Institute of Technology, VIT University, Vellore (Tamil Nadu). India.
3Preetha K.S, Department of Electronics Engineering, Vellore Institute of Technology, VIT University, Vellore (Tamil Nadu). India.
Manuscript received on 20 July 2016 | Revised Manuscript received on 30 July 2016 | Manuscript published on 30 July 2016 | PP: 1-3 | Volume-5 Issue-3 , July 2016 | Retrieval Number: C1601075316©BEIESP
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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: Different fall-recognition arrangements have been already proposed to make a dependable observation framework for elderly individuals with high necessities on exactness, affectability and specificity. In this paper, an improved fall recognition framework is proposed for elderly individual observing that depends on keen sensors worn on the body and working through purchaser home systems. With treble limits, inadvertent falls can be distinguished in the home social insurance environment. By using data assembled from an accelerometer, cardio tachometer and shrewd sensors, the effects of falls can be logged and recognized from ordinary every day exercises. The proposed framework has been conveyed in a model framework as itemized in this paper
Keywords: ARM, Pulse sensor, GSM, GPS, MMA7660FC MEMS Accelerometer, LPC2148 Microcontroller
Scope of the Article: Logic, Functional programming and Microcontrollers for IoT