Identification of Behaviour in Freely Moving Dogs (Canis familiaris) Using Inertial Sensors

Abstract

Monitoring and describing the physical movements and body postures of animals is one of the most fundamental tasks of ethology. The more precise the observations are the more sophisticated the interpretations can be about the biology of a certain individual or species. Animal-borne data loggers have recently contributed much to the collection of motion-data from individuals, however, the problem of translating these measurements to distinct behavioural categories to create an ethogram is not overcome yet. The objective of the present study was to develop a “behaviour tracker”: a system composed of a multiple sensor data-logger device (with a tri-axial accelerometer and a tri-axial gyroscope) and a supervised learning algorithm as means of automated identification of the behaviour of freely moving dogs. We collected parallel sensor measurements and video recordings of each of our subjects (Belgian Malinois, N=12; Labrador Retrievers, N=12) that were guided through a predetermined series of standard activities. Seven behavioural categories (lay, sit, stand, walk, trot, gallop, canter) were pre-defined and each video recording was tagged accordingly. Evaluation of the measurements was performed by support vector machine (SVM) classification. During the analysis we used different combinations of independent measurements for training and validation (belonging to the same or different individuals or using different training data size) to determine the robustness of the application. We reached an overall accuracy of above 90% perfect identification of all the defined seven categories of behaviour when both training and validation data belonged to the same individual, and over 80% perfect recognition rate using a generalized training data set of multiple subjects. Our results indicate that the present method provides a good model for an easily applicable, fast, automatic behaviour classification system that can be trained with arbitrary motion patterns and potentially be applied to a wide range of species and situations.

Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

“…Machine learning algorithms have regularly been used to classify animal behaviour from accelerometry data, with varying levels of success [10, 20, 46]. With a range of algorithms available and the wide array of problems to which they can be applied, it can be overwhelming to be able to select an appropriate method that will provide the greatest accuracy [17].…”

“…Previously it has been shown that with dogs there were no differences behaviour prediction in inter-breed comparisons [46]. It is suggested that the lack of difference in body morphology would explain the lack of difference.…”

Use of bio-loggers to characterize red fox behavior with implications for studies of magnetic alignment responses in free-roaming animals

“…and therefore were used to develop a behavioral classifier that would satisfy the focus of the current study (i.e., mousing behavior) and identify other functionally relevant behaviors for future bio-logging studies in red foxes. Trotting was defined as moving through the arena at a pace where two of the four paws were not in contact with the ground, but without the rapid horizontal accelerations and high rate of speed associated with sprinting behaviors (see Additional file 7: Video file S6) [6]. Foraging was defined as the fox slowly moving through the arena (slower pace than a walk) with its head and snout toward the ground searching for food using olfactory and visual cues (see Additional file 8: Video file S7).…”

“…Among the many sensor options, triaxial accelerometers can yield a wealth of valuable information about movements in three-dimensional space, but need sophisticated analysis techniques to be properly interpreted [4]. There is no consensus on the statistical techniques to use in extracting behavioral data from accelerometer signatures; the available methods have been shown to have varying degrees of success in identifying differences in behavior and posture across a range of diverse kinematic patterns [5,6]. In addition to accelerometers, bio-logging devices equipped with triaxial magnetometers are now available, providing researchers with a continuous record of the alignment of the sensor with respect to magnetic north.…”

Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry

“…There have been several attempts to evaluate the accuracies of different machine learning methods [7,13,15]. However, due to vastly distinct dynamic movement of different animal species, it is unlikely that there will ever be a universal set template for creating ethograms from accelerometry [16,17]. Instead, a new machine learning method described here may afford a solution to the problem of method selection.…”