Meeting Abstract
While animals are capable of moving at a broad range of speeds within each gait, they tend to use a relatively narrow set of preferred speeds. Explanations for this behaviour include maximising the efficiency of locomotion and minimising stresses on the musculoskeletal system. Previous work has investigated the use of preferred speeds, in a variety of animals, with measurements made on treadmills and observations of free ranging animals. These studies, however, have been limited by the accuracy with which speed could be measured, by the amount of data that could be collected and, especially in free ranging animals, with difficulty in determining gait. To address these issues, we have developed and deployed animal tracking collars containing a high resolution and accurate Global Positioning System and inertial (three axis accelerometer, gyroscope and magnetometer) sensors. Furthermore, we have shown how the application of a relatively simple unsupervised machine learning method can be used to classify gait using data from only the vertical axis accelerometer. The tracking collars have been used to collect data from lion (Panthera leo), African wild dog (Lycaon pictus) and cheetah (Acinonyx jubatus) in the Okavango Delta in Northern Botswana over a period of several months. Our equipment and analysis methods have enabled us to show how these free ranging animals utilise distinct preferred speeds within their different gaits and fit the hypothesis of dynamic similarity.
