Knowledge engineering for unsupervised canine posture detection from IMU data

ABSTRACT

Training animals is a process that requires a significant investment of time and energy on the part of the trainer. One of the most basic training tasks is to train dogs to perform postures on cue. While it might be easy for a human trainer to see when an animal has performed the desired posture, it is much more difficult for a computer to determine this. Most work in this area uses accelerometer and/or gyroscopic data to produce data from an animal's current state, but this has limitations. Take for example a normal standing posture. From an accelerometer's perspective, it closely resembles the "laying down" posture, but the posture can look very different if the animal is standing still, versus walking, versus running, and might look completely different from a "standing on incline" posture. A human trainer can instantly tell the difference between these postures and behaviors, but the process is much more difficult for a computer.

This paper demonstrates several algorithms for recognizing canine postures, as well as a system for building a computational model of a canine's potential postures, based solely on skeletal measurements. Existing techniques use labeled data, which can be difficult to acquire. We contribute a new technique for unsupervised posture detection, and compare the supervised technique to our new, unsupervised technique. Results indicate that the supervised technique performs with a mean 82.06% accuracy, while our unsupervised approach achieves a mean 74.25% accuracy, indicating that in some cases, our new unsupervised technique is capable of achieving comparable performance.

References

1. Machine Learning Group at the University of Waikato. 2015. Weka 3 Data Mining Software In Java. http://www.cs.waikato.ac.nz/ml/weka/index.html. (July 2015).
2. Bob Bailey and Parvene Farhoody. 2015. Bailey-Farhoody Discrimination Workshop. http://behaviormatters.com/workshops/. (June 2015).
3. Alper Bozkurt, David L. Roberts, Barbara L. Sherman, Rita Brugarolas, Sean Mealin, John Majikes, Pu Yang, and Robert Loftin. 2014. Towards Cyber-Enhanced Working Dogs for Search and Rescue. IEEE Intelligent Systems 29, 6 (2014), 32--39.
4. Rita Brugarolas, Robert T Loftin, Pu Yang, David L Roberts, Barbara Sherman, and Alper Bozkurt. 2013. Behavior recognition based on machine learning algorithms for a wireless canine machine interface. In 2013 IEEE International Conference on Body Sensor Networks (BSN). IEEE, 1--5.
5. Rita Brugarolas, David Roberts, Barbara Sherman, and Alper Bozkurt. 2012. Posture estimation for a canine machine interface based training system. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. IEEE, 4489--4492.
6. Rita Brugarolas, David Roberts, Barbara Sherman, and Alper Bozkurt. 2013. Machine learning based posture estimation for a wireless canine machine interface. In 2013 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS). IEEE, 10--12.
7. Punchthrough Designs. Accessed: 07/06/15. Light Blue Bean. https://punchthrough.com/bean/. (Accessed: 07/06/15).
8. Linda Gerencsér, Gábor Vásárhelyi, Máté Nagy, Tamas Vicsek, and Adam Miklósi. 2013. Identification of behaviour in freely moving dogs (Canis familiaris) using inertial sensors. PloS one 8, 10 (2013), e77814.
9. Bernard D Hansen, B Duncan X Lascelles, Bruce W Keene, Allison K Adams, and Andrea E Thomson. 2007. Evaluation of an accelerometer for at-home monitoring of spontaneous activity in dogs. American journal of veterinary research 68, 5 (2007), 468--475.
10. John A. Hartigan. 1975. Clustering Algorithms. John Wiley & Sons, Inc., New York, New York, USA.
11. Tin Kam Ho. 1995. Random Decision Forest. In Proceedings of the 3rd International Conference on Document Analysis and Recognition. 278--282.
12. Stephen J Preece, John Y Goulermas, Laurence PJ Kenney, Dave Howard, Kenneth Meijer, and Robin Crompton. 2009. Activity identification using body-mounted sensorsa review of classification techniques. Physiological measurement 30, 4 (2009), R1.
13. Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, and Michael L Littman. 2005. Activity recognition from accelerometer data. In AAAI, Vol. 5. 1541--1546.
14. Cristina Ribeiro, Alexander Ferworn, Mieso Denko, and James Tran. 2009. Canine pose estimation: A computing for public safety solution. In Computer and Robot Vision, 2009. CRV'09. Canadian Conference on. IEEE, 37--44.
15. 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.