Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis)

Abstract

Background:The ability to study animal behaviour is important in many fields of science, including biology, behavioural ecology and conservation. Behavioural information is usually obtained by attaching an electronic tag to the animal and later retrieving it to download the measured data. We present an animal-borne behaviour classification system, which captures and automatically classifies three-dimensional accelerometer data in real time. All computations occur on specially designed biotelemetry tags while attached to the animal. This allows the probable behaviour to be transmitted continuously, thereby providing an enhanced level of detail and immediacy. Results:The performance of the animal-borne automatic behaviour classification system is presented for sheep and rhinoceros. For sheep, a classification accuracy of 82.40% is achieved among five behavioural classes (standing, walking, grazing, running and lying down). For rhinoceros, an accuracy of 96.10% is achieved among three behavioural classes (standing, walking and lying down). The estimated behaviour was established approximately every 5.3 s for sheep and 6.5 s for rhinoceros. Conclusions:We demonstrate that accurate on-animal real-time behaviour classification is possible by successful design, implementation and deployed on sheep and rhinoceros. Since the bandwidth required to transmit the behaviour class is lower than that which would be required to transmit the accelerometer measurements themselves, this system is better suited to low-power and error-prone data communication channels that may be expected in the animals habitat.

Horsing Around—A Dataset Comprising Horse Movement

“…After training, the classifier can classify unlabeled raw data-samples into the learned activity categories. Recently, various studies utilized IMUs for AAR regarding: wildlife [3][4][5][6][7][8][9], livestock [1,2,[10][11][12][13][14][15][16][17][18], and pets [19][20][21].…”

Evaluating Contributions of Recent Tracking-Based Animal Movement Ecology to Conservation Management

“…In contrast, it would be far more difficult to collect such data from ecologically similar but non-colonial California condors that often occupy more remote and roadless areas (Poessel et al, 2018). This limitation explains why accelerometry and animal-borne video studies are often performed on wildlife that are faithfully colonial or easily recaptured, or on captive or domestic animals (Moll et al, 2007;Loyd et al, 2013;McGregor et al, 2015;den Uijl et al, 2017;Hernández-Pliego et al, 2017;Hicks et al, 2017;le Roux et al, 2017).…”

Machine learning goes wild: Using data from captive individuals to infer wildlife behaviour

“…One strategy that has been tested with continuously recorded data is to apply a moving window to partition the acceleration data and to compute summary statistics for each of the resulting segments. In different studies, these windows could partially overlap or not overlap at all (Hokkanen et al, 2011;Lush et al, 2016;le Roux et al, 2017). An application example very similar to our approach is the assessment of car driver aggressiveness using continuous data by Ferreira et al (2017).…”