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Semi-supervised classification of static canine postures using the Microsoft Kinect

Authors:

Sean Mealin,

Ignacio X. Domínguez,

David L. RobertsAuthors Info & Claims

ACI '16: Proceedings of the Third International Conference on Animal-Computer Interaction

Article No.: 16, Pages 1 - 4

https://doi.org/10.1145/2995257.3012024

Published: 15 November 2016 Publication History

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Abstract

3D sensing hardware, such as the Microsoft Kinect, allows new interaction paradigms that would be difficult to accomplish with traditional RGB cameras alone. One basic step in realizing these new methods of animal-computer interaction is posture and behavior detection and classification. In this paper, we present a system capable of identifying static postures for canines that does not rely on hand-labeled data at any point during the process. We create a model of the canine based on measurements automatically obtained in from the first few captured frames, reducing the burden on users. We also present a preliminary evaluation of the system with five dogs, which shows that the system can identify the "standing," "sitting," and "lying" postures with approximately 70%, 69%, and 94% accuracy, respectively.

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References

[1]

Rita Brugarolas, David L. Roberts, Barbara Sherman, and Alper Bozkurt. 2013. Machine learning based posture estimation for a wireless canine machine interface. In Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS), 2013 IEEE Topical Conference on. IEEE, 10--12.

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[2]

Melody Moore Jackson, Clint Zeagler, Giancarlo Valentin, Alex Martin, Vincent Martin, Adil Delawalla, Wendy Blount, Sarah Eiring, Ryan Hollis, Yash Kshirsagar, and others. 2013. FIDO-facilitating interactions for dogs with occupations: wearable dog-activated interfaces. In Proceedings of the International Symposium on Wearable Computers. ACM, 81--88.

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[3]

Sean Mealin, Steven Howell, and David L. Roberts. 2016. Towards Unsupervised Canine Posture Classification via Depth Shadow Detection and Infrared Reconstruction for Improved Image Segmentation Accuracy. In Proceedings of The 5th International Conference on Biomimetic and Biohybrid Systems (LM 2016). Springer, 155--166.

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Patricia Pons, Javier Jaén, and Alejandro Catalá. 2016. Detecting Animals' Body Postures Using Depth-Based Tracking Systems. In Animal Computer Interaction Symposia - Measuring Behavior.

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Michael Winters, Rita Brugarolas, John Majikes, Sean Mealin, Sherrie Yuschak, Barbara Sherman, Alper Bozkurt, and David L. Roberts. 2015. Knowledge Engineering for Unsupervised Canine Posture Detection from IMU Data. In Proceedings of The Second International Congress on Animal Human Computer Interaction (ACI 2015) at the 12th International Conference on Advances in Computer Entertainment Technology (ACE 2015).

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Mariko Yamamoto, Takefumi Kikusui, and Mitsuaki Ohta. 2009. Influence of delayed timing of owners' actions on the behaviors of their dogs, Canis familiaris. Journal of Veterinary Behavior: Clinical Applications and Research 4, 1 (2009), 11--18.

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Cited By

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Schork IZamansky AFarhat Nde Azevedo CYoung R(2024)Automated Observations of Dogs’ Resting Behaviour Patterns Using Artificial Intelligence and Their Similarity to Behavioural ObservationsAnimals10.3390/ani1407110914:7(1109)Online publication date: 4-Apr-2024
https://doi.org/10.3390/ani14071109
Marcato MTedesco SO’Mahony CO’Flynn BGalvin P(2023)Machine learning based canine posture estimation using inertial dataPLOS ONE10.1371/journal.pone.028631118:6(e0286311)Online publication date: 21-Jun-2023
https://doi.org/10.1371/journal.pone.0286311
Foster MWu TRoberts DBozkurt A(2022)Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working DogsSensors10.3390/s2219763122:19(7631)Online publication date: 8-Oct-2022
https://doi.org/10.3390/s22197631
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Index Terms

Semi-supervised classification of static canine postures using the Microsoft Kinect
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
2. Human-centered computing
  1. Human computer interaction (HCI)

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Information

Published In

ACI '16: Proceedings of the Third International Conference on Animal-Computer Interaction

November 2016

116 pages

ISBN:9781450347587

DOI:10.1145/2995257

General Chair:
Clara Mancini
The Open University, UK

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2016

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ACI '16

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ACI '16: Third International Conference on Animal-Computer Interaction

November 15 - 17, 2016

Milton Keynes, United Kingdom

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Cited By

View all

Schork IZamansky AFarhat Nde Azevedo CYoung R(2024)Automated Observations of Dogs’ Resting Behaviour Patterns Using Artificial Intelligence and Their Similarity to Behavioural ObservationsAnimals10.3390/ani1407110914:7(1109)Online publication date: 4-Apr-2024
https://doi.org/10.3390/ani14071109
Marcato MTedesco SO’Mahony CO’Flynn BGalvin P(2023)Machine learning based canine posture estimation using inertial dataPLOS ONE10.1371/journal.pone.028631118:6(e0286311)Online publication date: 21-Jun-2023
https://doi.org/10.1371/journal.pone.0286311
Foster MWu TRoberts DBozkurt A(2022)Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working DogsSensors10.3390/s2219763122:19(7631)Online publication date: 8-Oct-2022
https://doi.org/10.3390/s22197631
Wang HAtif OTian JLee JPark DChung Y(2022)Multi-level Hierarchical Complex Behavior Monitoring System for Dog Psychological Separation Anxiety SymptomsSensors10.3390/s2204155622:4(1556)Online publication date: 17-Feb-2022
https://doi.org/10.3390/s22041556
Lee HIhm SPark SPark Y(2021)An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ BehaviorsApplied Sciences10.3390/app11221105011:22(11050)Online publication date: 22-Nov-2021
https://doi.org/10.3390/app112211050
Fux AZamansky ABleuer-Elsner Svan der Linden DSinitca ARomanov SKaplun D(2021)Objective Video-Based Assessment of ADHD-Like Canine Behavior Using Machine LearningAnimals10.3390/ani1110280611:10(2806)Online publication date: 26-Sep-2021
https://doi.org/10.3390/ani11102806
Farhat N(2021)Objective Assessment of Movement for Canine NeurologyProceedings of the Eight International Conference on Animal-Computer Interaction10.1145/3493842.3493898(1-4)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1145/3493842.3493898
Zamansky ASinitca ALinden DKaplun D(2021)Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine LearningProcedia Computer Science10.1016/j.procs.2021.04.187186(661-668)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.04.187
Byrne CLogas J(2021)The Future of Technology and Computers in Veterinary MedicineDiagnostics and Therapy in Veterinary Dermatology10.1002/9781119680642.ch26(245-250)Online publication date: 8-Oct-2021
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Klishkovskaia TAksenov ASinitca AZamansky AMarkelov OKaplun D(2020)Development of Classification Algorithms for the Detection of Postures Using Non-Marker-Based Motion Capture SystemsApplied Sciences10.3390/app1011402810:11(4028)Online publication date: 10-Jun-2020
https://doi.org/10.3390/app10114028
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