Abstract
Movement data were collected at a riding stable over seven days. The dataset comprises data from 18 individual horses and ponies with 1.2 million 2-s data samples, of which 93,303 samples have been tagged with labels (labeled data). Data from 11 subjects were labeled. The data from six subjects and six activities were labeled more extensively. Data were collected during horse riding sessions and when the horses freely roamed the pasture over seven days. Sensor devices were attached to a collar that was positioned around the neck of horses. The orientation of the sensor devices was not strictly fixed. The sensors devices contained a three-axis accelerometer, gyroscope, and magnetometer and were sampled at 100 Hz.
Highlights
Activities from animals can be recognized from motion data [1,2]
Most Animal Activity Recognition (AAR) approaches utilize motion data that are recorded with Inertial Measurement Units (IMUs)
An IMU generally consists of an accelerometer, gyroscope, and magnetometer, which measure acceleration, angular velocity, and magnetism, respectively
Summary
Activities from animals can be recognized from motion data [1,2]. The advent of small, lightweight, and low-power electronics has propelled research in Animal Activity Recognition (AAR). The paper demonstrates the effect of increased complexity in AAR, parameter tuning, and class balancing on the classification performance and identifies open research challenges for AAR Most of this dataset is unlabeled data (denoted as null and unknown in the dataset). The aim of publicly releasing and describing our dataset is to allow other researchers to improve AAR methods and benchmark novel approaches to unsupervised representation learning for AAR. This dataset could be useful for research related to: gait analysis and comparison, feature selection for AAR, and transfer learning. The dataset might be valuable to improve AAR methods for other quadruped animals within the Equidae family, such as zebras or donkeys
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