Dataset

Abstract

We describe and analyze a dataset that comprises horse movement. Data was collected during horse riding sessions and when the horses freely roamed the pasture over 7 days. The dataset comprises 1.8 million 2-second data samples from 18 individual horses, of which 93303 samples from 11 subjects were labeled. Sensor devices were attached to a collar around the neck of the horses while the orientation was not fixed. The devices contained a 3-axis accelerometer, gyroscope, and magnetometer that were sampled at 100 Hz. To demonstrate how this dataset can be used, we evaluated a Naive Bayes classifier with leave-one-out validation. Our results show that a performance of 90 % accuracy can be achieved using only the 3D acceleration vector as input. Furthermore, we demonstrate the effect of increased complexity, parameter tuning, and class balancing on classification performance and identify open research challenges. The complete dataset is available online with open access at the 4TU.Centre for Research Data [9]. CCS CONCEPTS• Information systems → Data mining; • Theory of computation → Machine learning theory.

Horsing Around—A Dataset Comprising Horse Movement

“…In total, 17 different activities exercised by the horses were observed and annotated. This dataset has been used to evaluate a Naive Bayes (NB) classifier [23]. The paper briefly describes the dataset and shows that an AAR performance of 90 % accuracy can be achieved using only the 3D acceleration vector as input.…”

“…Therefore, these activities represent the largest part of the labeled dataset. Six activities from six subjects were annotated more extensively so that leave-one-out validation can be used for a subset of subjects and activities in [23,28]. Figure 4 shows the distribution of the labeled data using three summary statistics for each 2-s window of data: frequency entropy, the frequency component with the largest magnitude, and standard deviation.…”

Synchronization between Sensors and Cameras in Movement Data Labeling Frameworks

“…In order to label movement data of sheep [6], goats [7], and horses [8,9], a labeling framework was developed using a Matlab GUI [12]. The code of the framework is publicly available [5].…”

Hiding in the deep : online animal activity recognition using motion sensors and machine learning

“…A large amount of additional unlabeled data makes this dataset particularly interesting for unsupervised AAR research. The datasets and their descriptions have appeared in [70,71,77,83]…”

“…For example, the dataset might be valuable to improve AAR methods for other quadruped animals within the Equidae family, such as zebras or donkeys. The dataset was publicly released and described to allow other researchers to improve AAR methods and benchmark novel approaches to unsupervised representation learning for AAR [75,77,83]. The complete dataset is available online with open access at the 4TU.Centre for Research Data [75].…”