Dataset: Multi Sensor-Orientation Movement Data of Goats

Jacob Wilhelm Kamminga (Photographer)

Research output: Non-textual formDigital or Visual ProductsOther research output

Abstract

This is a labeled dataset. Motion data were collected from six sensor nodes that were fixed with different orientations to a collar around the neck of goats. These six sensor nodes simultaneously, with different orientations, recorded various activities performed by the goat. We recorded the activities of five different goats on two farms in the Netherlands. We used a 3-axis accelerometer, high-impact accelerometer, gyroscope, and magnetometer. All sensors were sampled at 100 Hz.

For more details please see the readme file in the dataset.
Original languageEnglish
PublisherDANS easy
Media of outputOnline
DOIs
Publication statusPublished - 26 Mar 2018

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Accelerometers
Sensor nodes
Gyroscopes
Sensors
Magnetometers
Farms

Keywords

  • Animal Activity Recognition
  • Feature Selection
  • Movement Data
  • Goats

Cite this

@misc{228947c900f4421bb71ac4ea9ec9ac1f,
title = "Dataset: Multi Sensor-Orientation Movement Data of Goats",
abstract = "This is a labeled dataset. Motion data were collected from six sensor nodes that were fixed with different orientations to a collar around the neck of goats. These six sensor nodes simultaneously, with different orientations, recorded various activities performed by the goat. We recorded the activities of five different goats on two farms in the Netherlands. We used a 3-axis accelerometer, high-impact accelerometer, gyroscope, and magnetometer. All sensors were sampled at 100 Hz.For more details please see the readme file in the dataset.",
keywords = "Animal Activity Recognition , Feature Selection, Movement Data , Goats",
author = "Kamminga, {Jacob Wilhelm}",
note = "The datasets contained within in this archive can only be used with citing the following paper: Jacob W. Kamminga, Duv V. Le, Jan Pieter Meijers, Helena C. Bisby, Nirvana Meratnia, and Paul J.M. Havinga. Robust sensor-orientation-independent feature selection for animal activity recognition on collar tags. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2(1), March 2018",
year = "2018",
month = "3",
day = "26",
doi = "10.17026/dans-xhn-bsfb",
language = "English",
publisher = "DANS easy",
address = "Netherlands",

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Dataset: Multi Sensor-Orientation Movement Data of Goats. Kamminga, Jacob Wilhelm (Photographer). 2018. DANS easy.

Research output: Non-textual formDigital or Visual ProductsOther research output

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N1 - The datasets contained within in this archive can only be used with citing the following paper: Jacob W. Kamminga, Duv V. Le, Jan Pieter Meijers, Helena C. Bisby, Nirvana Meratnia, and Paul J.M. Havinga. Robust sensor-orientation-independent feature selection for animal activity recognition on collar tags. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2(1), March 2018

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N2 - This is a labeled dataset. Motion data were collected from six sensor nodes that were fixed with different orientations to a collar around the neck of goats. These six sensor nodes simultaneously, with different orientations, recorded various activities performed by the goat. We recorded the activities of five different goats on two farms in the Netherlands. We used a 3-axis accelerometer, high-impact accelerometer, gyroscope, and magnetometer. All sensors were sampled at 100 Hz.For more details please see the readme file in the dataset.

AB - This is a labeled dataset. Motion data were collected from six sensor nodes that were fixed with different orientations to a collar around the neck of goats. These six sensor nodes simultaneously, with different orientations, recorded various activities performed by the goat. We recorded the activities of five different goats on two farms in the Netherlands. We used a 3-axis accelerometer, high-impact accelerometer, gyroscope, and magnetometer. All sensors were sampled at 100 Hz.For more details please see the readme file in the dataset.

KW - Animal Activity Recognition

KW - Feature Selection

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