Dataset Information

Description of movement sensor dataset for dog behavior classification.

ABSTRACT: 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.

SUBMITTER: Vehkaoja A

PROVIDER: S-EPMC8777071 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Description of movement sensor dataset for dog behavior classification.

Vehkaoja Antti A Somppi Sanni S Törnqvist Heini H Valldeoriola Cardó Anna A Kumpulainen Pekka P Väätäjä Heli H Majaranta Päivi P Surakka Veikko V Kujala Miiamaaria V MV Vainio Outi O

Data in brief 20220111

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 ( ...[more]

PMID: 35079615

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Project description:Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST's ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST's ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST's response to less resolved data. Finally, we evaluate RST's performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

Project description:Sperm morphology is an important component of dog fertility analysis. Visual examination is the gold standard technique for evaluation, but large variations within and across evaluators have been documented. Databases of sperm morphology images are valuable practical aids for training and performance testing and can improve sperm classification standardisation; however, such databases are only useful when the material is expertly classified to serve as a reference. The objective of this study was to build an expertly annotated database of sperm images and to generate an illustrated guide with examples of the different sperm morphology presentations to promote standardisation in dog sperm morphology classification. Semen samples (n = 42) were obtained either from dogs presented for fertility evaluation or from semen doses used for artificial insemination. For image acquisition, wet preparations were prepared and evaluated using phase-contrast optics under 1000× magnification. Selected images were cropped so that only one sperm was included in each file, and a database of 8817 images was created. The images were classified by three veterinarians, specialists in animal reproduction (Diplomates American College of Theriogenologists) according to different criteria. Agreement among evaluators was the criterion used to create a database with reconciled classification. Complete agreement on defect category and morphological classification was observed on 70.2% of the images, and at least two veterinarians agreed on the classification of another 27.4% of the images; therefore, the reconciled database classification was based on the consensus of at least two veterinarians on 97.5% of the images. The percentage of morphologically normal sperm in the reconciled database was 57%, with the primary defect involving the sperm head, midpiece, or tail in 18%, 20%, and 5% of the cases, respectively. Among defective sperm, 39% had a single defect and 4% had multiple defects. When the total prevalence of specific defects was computed, the reconciled database contained 18% sperm head abnormalities, 22% midpiece abnormalities, and 7% tail abnormalities. Comprehensive illustrations of the variation in normal and abnormal dog sperm morphology from the database built based on reconciled expert classification are provided for reference.

Project description:IntroductionRegular physical activity is important for children's physical and mental health, yet many children do not achieve recommended amounts of physical activity. Dog ownership has been associated with increased physical activity in children, however, there have been no longitudinal studies examining this relationship. This study used data from the Play Spaces and Environments for Children's Physical Activity (PLAYCE) cohort study to examine the longitudinal effects of dog ownership status on children's movement behaviours.MethodsChange in dog ownership from preschool (wave 1, age 2-5) to fulltime school (wave 2, age 5-7) was used as a natural experiment with four distinct dog ownership groups: continuing non-dog owners (n = 307), continuing dog owners (n = 204), dog acquired (n = 58), and dog loss (n = 31; total n = 600). Daily movement behaviours, including physical activity, sedentary time, sleep, and screen time, were measured using accelerometry and parent-report surveys. Differences between groups over time and by sex were tested using linear mixed effects regression models.ResultsGirls who acquired a dog increased their light intensity activities and games by 52.0 min/day (95%CI 7.9, 96.0) and girls who lost a dog decreased their light intensity activities and games by 62.1 min/day (95%CI -119.3, -4.9) compared to no change among non-dog owners. Girls and boys who acquired a dog increased their unstructured physical activity by 6.8 (95%CI 3.2, 10.3) and 7.1 (95%CI 3.9, 10.3) occasions/week, compared to no changes among non-dog owners. Girls and boys who lost a dog reduced their unstructured physical activity by 10.2 (95%CI -15.0, -5.3) and 7.7 (95%CI -12.0, -3.5) occasions/week. Girls who lost a dog decreased their total physical activity by 46.3 min/day (95%CI -107.5, 14.8) compared to no change among non-dog owners. Continuing dog ownership was typically not associated with movement behaviours. Dog ownership group was not associated with changes in sleep and had mixed associations with screen time.ConclusionThe positive influence of dog ownership on children's physical activity begins in early childhood and differs by child sex. Further research should examine the specific contributions dog-facilitated physical activity makes to children's overall physical activity, including the intensity and duration of dog walking and play.

Dataset Information

Description of movement sensor dataset for dog behavior classification.

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Description of movement sensor dataset for dog behavior classification.

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