title: Understanding and interpretation of wearable inertial sensor output in human monitoring
Creators: name: Worsey, Matthew T
Publisher: name: Griffith University
Year: 2022
Subjects: subject: inertial sensors
subject: elite sport
subject: signal processing techniques

description: Although the use of inertial sensors to deliver measures of sport performance can be found in abundance within the published scientific literature, the technology is still, only slowly transitioning into the elite sporting environment. This is probably because, without knowledge of signal processing techniques and machine learning models, the insights provided by inertial sensor data are obscure. Commercial systems that provide post-processed metrics using proprietary algorithms are available, however; this comes with a substantial increase in cost and limited access to the raw data. This thesis can serve as a guide for the sport performance analyst community so that performance monitoring algorithms can be implemented using affordable inertial sensor systems. The thesis reports hardware requirements of inertial sensors in various sport settings, and signal processing techniques required to gain value from the data. It documents the mathematical principles of supervised machine learning methods from a sporting perspective and highlights synergistic biofeedback technologies that can be used in conjunction with inertial sensors to validate novel algorithms, and to generate more insightful athletic performance feedback. Chapter 1 introduces the reader to the application of wearable sensors in the modern era and how sensors are used to monitor human activity, both in general living and in sports performance. Chapter 2 details important factors to consider when implementing inertial sensors into a training environment, such as sensor characteristics, signal processing techniques, sensor fusion, machine learning models and synergistic biofeedback technologies. Chapter 3 systematically assesses the literature using inertial sensors in both combat sports and elite rowing with respect to i) The number of sensors used and their properties, ii) Sensor placement, iii) Algorithmic implementation, iv) The outputted performance features, v) details regarding study design, and vi) Synergistic technologies. Chapter 4 reports the performance of different supervised machine learning models to classify impact (boxing punch) and cyclic (swimming stroke) sport-specific movements using features extracted from inertial sensors. Chapter 5 compares the performance of different supervised machine learning models to classify the sport-specific environment during running. Chapter 6 demonstrates the use of inertial sensors to deliver metrics associated with performance, safety and technique in swimming, soccer and rowing. Chapter concludes by tying all the chapters together and demonstrates how the individual outcomes from each chapter could be combined to generate an informative, athlete performance monitoring system using inertial sensors. Each novel algorithm presented was assessed using criteria to gauge the applicability to a training environment. The criteria evaluated consisted of the following questions and considerations: i) is the algorithm generalised (adaptable to other sports and applications) ii) Is the number of sensors required equal to or less than 3? iii) The requirements of external biofeedback technology iv) the use of optimal sensor locations from a training standpoint, and v) if the algorithm achieved acceptable performance. It is hoped that this thesis will accelerate the uptake of inertial sensors in the elite sporting environment and see their full potential realised.