Dataset: Horse Movement Data and Analysis of its Potential for Activity Recognition

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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.
Original languageEnglish
Publication statusPublished - 10 Nov 2019
Event2nd Workshop on Data Acquisition To Analysis, DATA 2019 - Columbus University, New York, United States
Duration: 10 Nov 201910 Nov 2019
Conference number: 2


Conference2nd Workshop on Data Acquisition To Analysis, DATA 2019
Abbreviated titleDATA
Country/TerritoryUnited States
CityNew York


  • Animals, Horses, Activity Recognition, Accelerometer, Gyroscope, Compass, IMU, Orientation Independent, Neck


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