Implementation of Cloud IoT - Based Patient Fall Detection System with GeoTagging using Tilt Sensors and Texas Instruments’ CC3200

Abstract

Objective: Coupled with an elderly population and extended life expectancy, disorder in the movement is one of the leading causes of functional disability. In this study we have designed an IoT-based Patient Fall Detection System (PFDS) using a low-cost sensor SW-520D, cloud computing technology and GeoTagging. Methods/Analysis: The position of the patient is detected by using tilt sensor which uses the rolling ball technology and is interfaced with the CC3200 microcontroller which is Wi-Fi on chip module using 802.11 b/g/n network protocol. The location of the patient can easily be tracked by GeoTagging technique. Findings: when the patient is tilted on the wheel chair/adjustable bed the tilt sensor detects the tilted motion and data is processed to the microcontroller and the data will be uploaded through Thing Speak cloud which is used for storing the real time values and is represented graphically. SIM 900A GSM module is used to send an alert if abnormality detected in the patient position and the location of the patient can easily be accessed using GeoTagging. Conclusion: In this study we have developed a cost-effective IoT-based PFDS that can access the data in real time through Thing Speak cloud and an alert signal is sent to the nursing attendant and hospital dashboard. Keywords: GeoTagging, Internet of Things (IoT), Patient Fall Detection System, Tilt Sensor, CC3200