Smartphone Wireless Gyroscope Platform for Machine Learning Classification of Hemiplegic Patellar Tendon Reflex Pair Disparity through a Multilayer Perceptron Neural Network

Abstract

The patellar tendon enables fundamental insight regarding neurological health status. Clinically observed dysfunction may warrant escalation to more advanced and expensive medical diagnostics. Conventionally clinicians apply an ordinal scale to quantify reflex response characteristics. However the reliability of ordinal scales is a subject of debate, and even highly skilled clinicians have disputed the observation of an asymmetric reflex pair. An alternative is the use of the wireless quantified reflex system, which features an impact pendulum attached to a reflex hammer for providing precisely targeted levels of potential energy with a smartphone (iPhone) equipped with software to function as a wireless gyroscope platform that can email a trial sample as an email attachment by wireless connectivity to the Internet. With notable attributes of the gyroscope signal recordings of the reflex response of a hemiplegic patellar tendon reflex pair observed a feature set is developed for machine learning classification. Using the multilayer perceptron neural network considerable classification accuracy is attained. The research implications reveal the potential of integrating machine learning with a wireless reflex quantification system that applies a smartphone (iPhone) as a wireless gyroscope platform.