Silvina, Agastya (2013) System (for) Tracking Equilibrium and Determining Incline (STEADI). Masters thesis, National University of Ireland Maynooth.

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Abstract

The goal of this project was to design and implement a smartphone-based wearable system to detect fall events in real time. It has the acronym STEADI. Rather than have expensive customised hardware STEADI was implemented in a cost effective manner using a generic mobile computing device. In order to detect the fall event, we propose a fall detector that uses the accelerometer available in a mobile phone. As for detecting a fall we mainly divide the system in two sections, the signal processing and classification. For the processing both a median filter and a high pass filter are used. A Median filter is used to amplify/enhance the signal by removing impulsive noise while preserving the signal shape while the High pass filter is used to emphasise transitions in the signal. Then, in order to recognize a fall event, our STEADI system implements two methods that are a simple threshold analysis to determine whether or not a fall has occurred (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared with using the threshold-based method alone. The Naïve-Bayes achieved a detection accuracy of 95% in overall. Furthermore, an external sensor is introduced in order to enhance its accuracy. In addition to the fall detection, the systems can also provide location information using Google Maps as to the whereabouts of the fall event using the available GPS on the smartphone and sends the message to the caretaker via an SMS.

Item Type:	Thesis (Masters)
Additional Information:	Taught Masters Thesis for the Erasmus Mundus MSc in Dependable Software Systems
Keywords:	Tracking Equilibrium; Determining Incline; STEADI;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	5338
Depositing User:	IR eTheses
Date Deposited:	03 Sep 2014 14:26
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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