Development and testing of a prototype reflex measurement system employing artificial neural networks

This paper presents the development, testing and performance evaluation of a patellar tendon reflex measurement system to provide a quantitative reflex evaluation for use by medical practitioners and in a telemedicine or E-medicine environment. A prototype was developed that makes use of XSens MTx orientation sensors, force-sensitive resistors and an electromyogram to measure the reflex response. Suitable parameters from the sensors were identified for analysis, and clinical testing was performed on 20 subjects to collect data to evaluate the system's performance. Subjective reflex evaluations were conducted by three medical doctors according to a standard reflex grading scale using video recordings of the tests. Multi-layer feed-forward (MLFF) artificial neural networks (ANNs) were used to analyze the collected data with the aim of pattern identification and reflex grading prediction. It was found that the MLFF network delivered the corresponding 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%). The use of ANNs to analyze a reflex measurement offers a repeatable and concise representation of the reflex that is familiar to doctors and can be developed for use in a general clinical setting or for telemedicine purposes.