Evaluating Sensor Placement and Feature Importance for Hurling Movement Classification

Human Activity Recognition (HAR) involves recognising and classifying human activities from data collected by sensors through machine learning (ML) techniques. The assessment of athletic movement via HAR has benefited sport performance analysis by identifying technical and tactical performance indicators. Hurling is a dynamic stick and ball invasion team sport that involves high-impact movements. Sensor placement and feature selection in HAR tasks impact the classification accuracy of the ML model during testing and training. This study aims to determine the optimal inertial measuring unit (IMU) sensor placement for recognizing hurling movements and to identify the most important features for accurate classification. Time-domain and frequency-domain features of accelerometer data were computed and were used to train and test three classification models: Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbour (k-NN). A RF model achieved the highest mean accuracy in th e recognition of four hurling specific movements, for sensors located at the forearm (86%) and the thigh (84%). Features extracted from the z-axis, specifically zero crossing rate (ZCR), standard deviation (STD), and root mean square (RMS) were most discriminative in classifying hurling sport movements with a RF model using a forearm-mounted IMU.