Locomotion analysis in horses and cows based on sensor signals

Abstract

The increasing number of possibilities to monitor both physical and mental health in animals comes with an increasing amount of data that needs to be processed and analyzed. In this thesis, biomechanical gait characteristics of horses and cows were measured with Inertial Measurement Units (IMUs) attached to the animals and different signal processing procedures were developed and validated of for this type of data. In Chapters 2 and 3, new algorithms were developed for the automatic detection of hoof-events from hoof-mounted IMUs, and the performance of these algorithms was evaluated against two existing and reliable methods, the force plate and optical motion capture system. In Chapter 4 and 5, explorative studies with IMUs attached to multiple anatomical locations of cows were described. These studies evaluated the changes in biomechanical gait characteristics with induced lameness and the feasibility of different attachment locations of the sensors. In these studies, the measurement methods and signal-analysis procedures developed for the horse were applied to cows. In Chapter 6, various aspects and challenges are described that come with the application of sensors and the development of signal analysis procedures for locomotion. Additionally, two unrelated case studies on sensors in pigs and monkeys are presented. In the future, machine learning algorithms might facilitate the use of IMU sensors for clinical applications and make IMUs easier to apply for the purpose of training and exercising and video imaging might be used in the clinical setting.