The influence of feature selection methods on exercise classification with inertial measurement units

Abstract

Inertial measurement unit (IMU) based systems are becoming increasingly popular in the classification of human movement. While research in the area has established the utility of various machine learning classification methods, there is a paucity of evidence investigating the effect of feature selection on classification efficacy. The aim of this study was therefore to investigate the influence of feature selection methodology on the classification accuracy of human movement data. The efficacy of four commonly used feature selection and classification methods were compared using four IMU human movement data sets. Optimisation of classification and features selection methodologies resulted in an overall improvement in F1 score of between 1–8% for all four data sets. The findings from this study illustrate the need for researchers to consider the effect classification and feature selection methodologies may have on system efficacy.