Using echo state networks to classify unscripted, real-world punctual activity

Abstract

This paper employs an Echo State Network (ESN) to classify unscripted, real-world, punctual activity using inertial sensor data collected from horse riders. ESN has been shown to be an effective blackbox classifier for spatio-temporal data and so we suggest that ESN could be useful as a classifier for punctual human activities and as a result a potential tool for wearable technologies. The aim of this study is to provide an example classifier, illustrating the applicability of ESN as a punctual activity classifier for the chosen problem domain. This is part of a wider set of work to build a wearable coach for equestrian sport.