Development of a system for monitoring changes in human gait

Simon Kozina (2010) Development of a system for monitoring changes in human gait. EngD thesis.

Abstract

On the market, there are more and more di®erent systems for monitoring elderly people. Most of the systems are expensive and inaccessible since they use very sophisticated monitoring equipment. Basic functions of monitoring could be done by reasonably priced sensors such as accelerometers. The object of this work was to develop the system for monitoring changes in human gait. We used data from accelerometers attached to the left and right ankle. Accelerations are represented in three-dimensional coordinate system. We realized the best solution to our problem was to divide data set into ¯xed time intervals. Without this assumption it would have been di±cult to deter- mine the right attributes for machine learning. To better understand the data in one interval, we divided walking into its primitive elements - steps. By using some gait characteristics we were able to conceive two algorithms. Together they enabled us to divide data into single steps. Eight di®erent attributes distinctive for walking were chosen. The next thing was an attempt to create universal training data set that could recognize the di®erence between normal walking and limping. Testing was done on di®erent time intervals. It proved successful to divide data into longer time intervals. By doing so we got better information about gait in one time interval. SVM proved to be the optimal machine learning model for the presented classi¯cation problem.

Item Type:

Thesis (EngD thesis)

Keywords:

on the market, there are more and more di®erent systems for monitoring elderly people. Most of the systems are expensive and inaccessible since they use very sophisticated monitoring equipment. Basic functions of monitoring could be done by reasonably priced sensors such as accelerometers. The object of this work was to develop the system for monitoring changes in human gait. We used data from accelerometers attached to the left and right ankle. Accelerations are represented in three-dimensional coordinate system. We realized the best solution to our problem was to divide data set into ¯xed time intervals. Without this assumption it would have been di±cult to deter- mine the right attributes for machine learning. To better understand the data in one interval, we divided walking into its primitive elements - steps. By using some gait characteristics we were able to conceive two algorithms. Together they enabled us to divide data into single steps. Eight di®erent attributes distinctive for walking were chosen. The next thing was an attempt to create universal training data set that could recognize the di®erence between normal walking and limping. Testing was done on di®erent time intervals. It proved successful to divide data into longer time intervals. By doing so we got better information about gait in one time interval. SVM proved to be the optimal machine learning model for the presented classi¯cation problem. gait monitoring, machine learning, accelerometer, accuracy assessment

Number of Pages:

Language of Content:

Slovenian

Mentor / Comentors:

Name and Surname	ID	Function
akad. prof. dr. Ivan Bratko	77	Mentor
prof. dr. Matjaž Gams	1109	Comentor

Link to COBISS:

http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(ID=00023986727)

Institution:

University of Ljubljana

Department:

Faculty of Computer and Information Science

Item ID:

1171

Date Deposited:

21 Sep 2010 15:49

Last Modified:

13 Aug 2011 00:37

URI:

http://eprints.fri.uni-lj.si/id/eprint/1171

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