Reflex sensors for telemedicine applications
Date
2008-03
Authors
Busch, Alexander Carlo
Journal Title
Journal ISSN
Volume Title
Publisher
Stellenbosch : University of Stellenbosch
Abstract
A solution is sought for the measurement of human deep tendon reflexes as part of
a comprehensive patient condition monitoring system for use in a telemedicine
context. This study focused on the development, testing and performance
evaluation of a prototype compact patellar tendon reflex measurement system that
is able to provide a quantitative reflex evaluation for use by medical practitioners
and in a telemedicine environment. A prototype system was developed that makes
use of Xsens MTx orientation sensors, force-sensing resistors and an
electromyogram (EMG) to measure the reflex response. Suitable parameters
identified for analysis included the change in pitch, angular velocity and
acceleration of the lower leg, the EMG response, the tendon impact, and various
latencies associated with these measurements. Other information considered
included the age, mass, and physical dimensions of the test subject. Clinical
testing was performed to collect data to evaluate the system performance.
Subjective reflex evaluations were conducted by three doctors according to a
standard reflex grading scale using video recordings of the tests. Self-organizing
maps and multi-layer feed-forward (MLFF) artificial neural networks (ANNs)
were used to analyze the collected data with the aim of pattern identification, data
classification and reflex grading prediction. It was found that the MLFF network
delivered the correct reflex grading with an accuracy of 85%, which was of the
same order as the rate of differences between the subjective reflex evaluations
performed by the doctors (80%). Furthermore, analysis of the data suggested that
certain parameters were not necessary for the autonomous evaluation, such as
EMG data and the tendon impact. The use of ANNs to analyze a reflex
measurement as proposed by this study offers an accurate, repeatable and concise
representation of the reflex that is familiar to doctors and suitable for use in a
general clinical setting or for telemedicine purposes.
Description
Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2008.
Keywords
Reflex, Telemedicine, Neural network, Dissertations -- Mechanical engineering, Theses -- Mechanical engineering, Tendon reflex -- Testing, Biomechanics