Description of movement sensor dataset for dog behavior classification
Vehkaoja, A., Somppi, S., Törnqvist, H., Valldeoriola Cardó, A., Kumpulainen, P., Väätäjä, H., Majaranta, P., Surakka, V., Kujala, M. V., & Vainio, O. (2022). Description of movement sensor dataset for dog behavior classification. Data in Brief, 40, Article 107822. https://doi.org/10.1016/j.dib.2022.107822
Published inData in Brief
© 2022 The Author(s). Published by Elsevier Inc.
Movement sensor data from seven static and dynamic dog behaviors (sitting, standing, lying down, trotting, walking, playing, and (treat) searching i.e. sniffing) was collected from 45 middle to large sized dogs with six degree-of-freedom movement sensors attached to the collar and the harness. With 17 dogs the collection procedure was repeated. The duration of each of the seven behaviors was approximately three minutes. The order of the tasks was varied between the dogs and the two repetitions (for the 17 dogs). The behaviors were annotated post-hoc based on the video recordings made with two camcorders during the tests with one second resolution. The annotations were accurately synchronized with the raw movement sensors data. The annotated data was originally used for training behavior classification machine learning algorithms for classifying the seven behaviors. The developed signal processing and classification algorithms are provided together with the raw measurement data and reference annotations. The description and results of the original investigation that the dataset relates to are found in: P. Kumpulainen, A. Valldeoriola Cardó, S. Somppi, H. Törnqvist, H. Väätäjä, P. Majaranta, Y. Gizatdinova, C. Hoog Antink, V. Surakka, M. V. Kujala, O. Vainio, A. Vehkaoja, Dog behavior classification with movement sensors placed on the harness and the collar, Applied Animal behavior Science, 241 (2021), 105,393. ...
Dataset(s) related to the publicationhttps://doi.org/10.17632/vxhx934tbn.2
Publication in research information system
MetadataShow full item record
Additional information about fundingThis research was funded by Business Finland, a Finnish funding agency for innovation, grant numbers 1665/31/2016, 1894/31/2016, 7244/31/2016 in the context of “Buddy and the Smiths 2.0” project.
Showing items with similar title or keywords.
Raita-Hakola, Anna-Maria; Pölönen, Ilkka (Copernicus Publications, 2022)The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the ...
Ärje, Johanna; Melvad, Claus; Jeppesen, Mads Rosenhøj; Madsen, Sigurd Agerskov; Raitoharju, Jenni; Rasmussen, Maria Strandgård; Iosifidis, Alexandros; Tirronen, Ville; Gabbouj, Moncef; Meissner, Kristian; Høye, Toke Thomas (Wiley, 2020)Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming ...
Prezja, Fabi (2018)In the field of artificial intelligence, supervised machine learning enables us to try to develop automatic recognition systems. In music information retrieval, training and testing such systems is possible with a variety ...
Kumpulainen, Pekka; Cardó, Anna Valldeoriola; Somppi, Sanni; Törnqvist, Heini; Väätäjä, Heli; Majaranta, Päivi; Gizatdinova, Yulia; Hoog, Antink Christoph; Surakka, Veikko; Kujala, Miiamaaria V.; Vainio, Outi; Vehkaoja, Antti (Elsevier BV, 2021)Dog owners’ understanding of the daily behaviour of their dogs may be enhanced by movement measurements that can detect repeatable dog behaviour, such as levels of daily activity and rest as well as their changes. The aim ...
Nieminen, Paavo (University of Jyväskylä, 2016)Machine learning tasks usually come with several mutually conﬂicting objectives. One example is the simplicity of the learning device contrasted with the accuracy of its performance after learning. Another common example ...