Towards Globalised Models for Exercise Classification using Inertial Measurement Units

Wearable sensors are becoming a popular method of objectively evaluating motor performance in various exercise tasks. A challenge in working with this motion capture data is the personalised nature of the data where one individual’s data may be different to that of others due to factors such as differences in exercise form. Hence global models generally perform poorly in this domain. The aim of this study is to investigate a technique to push personalised classification models into global models. In this research, a dataset of Inertial Measurement Unit data consisting of fatigued and non-fatigued running is used to employ a clustering strategy where participants are grouped together using a hierarchical clustering methodology. These clusters are used to form semi-globalised models. The investigation shows that this strategy can improve the global model performance by up to 20%.

I. Introduction

Running, while a popular form of exercise, is associated with a high risk of musculoskeletal injuries [1]. Wearable sensors such as Inertial Measurement Units (IMUs) can capture the changes in the kinematic behaviour as a person starts to get fatigued and hence can aid in the development of injury prevention strategies. The data that is obtained from these sensors are time series by nature.