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PLOS ONE  2013 

Identification of Behaviour in Freely Moving Dogs (Canis familiaris) Using Inertial Sensors

DOI: 10.1371/journal.pone.0077814

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Abstract:

Monitoring and describing the physical movements and body postures of animals is one of the most fundamental tasks of ethology. The more precise the observations are the more sophisticated the interpretations can be about the biology of a certain individual or species. Animal-borne data loggers have recently contributed much to the collection of motion-data from individuals, however, the problem of translating these measurements to distinct behavioural categories to create an ethogram is not overcome yet. The objective of the present study was to develop a “behaviour tracker”: a system composed of a multiple sensor data-logger device (with a tri-axial accelerometer and a tri-axial gyroscope) and a supervised learning algorithm as means of automated identification of the behaviour of freely moving dogs. We collected parallel sensor measurements and video recordings of each of our subjects (Belgian Malinois, N=12; Labrador Retrievers, N=12) that were guided through a predetermined series of standard activities. Seven behavioural categories (lay, sit, stand, walk, trot, gallop, canter) were pre-defined and each video recording was tagged accordingly. Evaluation of the measurements was performed by support vector machine (SVM) classification. During the analysis we used different combinations of independent measurements for training and validation (belonging to the same or different individuals or using different training data size) to determine the robustness of the application. We reached an overall accuracy of above 90% perfect identification of all the defined seven categories of behaviour when both training and validation data belonged to the same individual, and over 80% perfect recognition rate using a generalized training data set of multiple subjects. Our results indicate that the present method provides a good model for an easily applicable, fast, automatic behaviour classification system that can be trained with arbitrary motion patterns and potentially be applied to a wide range of species and situations.

References

[1]  Hooker S, Biuw M, McConnell B, Miller P, Sparling C (2007) Bio-logging science: logging and relaying physical and biological data using animal-attached tags. Deep Sea Res II Topical Stud Oceanogr 54: 177–182. doi:10.1016/j.dsr2.2007.01.001.
[2]  Rutz C, Hays GC (2009) New frontiers in biologging science. Biol Lett 5: 289–292. doi:10.1098/rsbl.2009.0089. PubMed: 19324624.
[3]  Kooyman G (2004) Genesis and evolution of bio-logging devices. Mem Natl Inst Polar Res. pp. 15–58
[4]  Yoda K, Naito Y, Sato K, Takahashi A, Nishikawa J et al. (2001) A new technique for monitoring the behaviour of free-ranging adelie penguins. J Exp Biol 204: 685–690. PubMed: 11171350.
[5]  Watanabe S, Izawa M, Kato A, Ropert-Coudert Y, Naito Y (2005) A new technique for monitoring the detailed behaviour of terrestrial animals: a case study with the domestic cat. Appl Anim Behav Sci 94: 117–131. doi:10.1016/j.applanim.2005.01.010.
[6]  Wilson R, Shepard E, Liebsch N (2008) Prying into the intimate details of animal lives: use of a daily diary on animals. Endangered Species Res 4: 123–137. doi:10.3354/esr00064.
[7]  Britt W, Miller J, Waggoner P, Bevly D, Hamilton J?Jr (2011) An embedded system for real-time navigation and remote command of a trained canine. Personal Ubiquitous Comput 15: 61–74. doi:10.1007/s00779-010-0298-4.
[8]  Green JA, Boyd IL, Woakes AJ, Green CJ, Butler PJ (2005) Do seasonal changes in metabolic rate facilitate changes in diving behaviour? J Exp Biol 208: 2581–2593. doi:10.1242/jeb.01679. PubMed: 15961744.
[9]  Knower?Stockard T, Heil J, Meir JU, Sato K, Ponganis KV et?al.. (2005) Air sac po2 and oxygen depletion during dives of emperor penguins. J Exp Biol 208: 2973–2980. doi:10.1242/jeb.01687. PubMed: 16043602.
[10]  Boyd I, Kato A, Ropert-Coudert Y (2004) Bio-logging science: sensing beyond the boundaries 58. Mem Natl Inst Polar Res Spec. pp. 1–14.
[11]  Lander M, Loughlin T, Logsdon M, VanBlaricom G, Fadely B (2010) Foraging effort of juvenile steller sea lions eumetopias jubatus with respect to heterogeneity of sea surface temperature. Endang Species Res 10: 145–158. doi:10.3354/esr00260.
[12]  Bograd S, Block B, Costa D, Godley B (2010) Biologging technologies: new tools for conservation. introduction. Endangered Species Res 10: 1–7. doi:10.3354/esr00269.
[13]  Sakamoto KQ, Sato K, Ishizuka M, Watanuki Y, Takahashi A et?al.. (2009) Can ethograms be automatically generated using body acceleration data from free-ranging birds? PLOS ONE 4: e5379. doi:10.1371/journal.pone.0005379. PubMed: 19404389.
[14]  Call K, Fadely B, Greig A, Rehberg M (2007) At-sea and on-shore cycles of juvenile steller sea lions (eumetopias jubatus) derived from satellite dive recorders: A comparison between declining and increasing populations. Deep Sea Res II Topical Stud Oceanogr 54: 298–310. doi:10.1016/j.dsr2.2006.11.016.
[15]  Block B, Costa D, Boehlert G, Kochevar R (2002) Revealing pelagic habitat use: the tagging of pacific pelagics program. Oceanol Acta 25: 255–266. doi:10.1016/S0399-1784(02)01212-4.
[16]  Ringgenberg N, Bergeron R, Devillers N (2010) Validation of accelerometers to automatically record sow postures and stepping behaviour. Appl Anim Behav Sci 128: 37–44. doi:10.1016/j.applanim.2010.09.018.
[17]  Marchioro G, Cornou C, Kristensen A, Madsen J (2011) Sows’ activity classification device using acceleration data–a resource constrained approach. Comput Electron Agric 77: 110–117. doi:10.1016/j.compag.2011.04.004.
[18]  Moreau M, Siebert S, Buerkert A, Schlecht E (2009) Use of a tri-axial accelerometer for automated recording and classification of goats’ grazing behaviour. Appl Anim Behav Sci 119: 158–170. doi:10.1016/j.applanim.2009.04.008.
[19]  Elliott K, Le?Vaillant M, Kato A, Speakman J, Ropert-Coudert Y (2013) Accelerometry predicts daily energy expenditure in a bird with high activity levels. Biol Lett 9.
[20]  Halsey LG, Jones TT, Jones DR, Liebsch N, Booth DT (2011) Measuring energy expenditure in sub-adult and hatchling sea turtles via accelerometry. PLOS ONE 6: e22311. doi:10.1371/journal.pone.0022311. PubMed: 21829613.
[21]  Wrigglesworth DJ, Mort ES, Upton SL, Miller AT (2011) Accuracy of the use of triaxial accelerometry for measuring daily activity as a predictor of daily maintenance energy requirement in healthy adult labrador retrievers. Am J Vet Res 72: 1151–1155. doi:10.2460/ajvr.72.9.1151. PubMed: 21879971.
[22]  Fourati H, Manamanni N, Afilal L, Handrich Y (2011) A nonlinear filtering approach for the attitude and dynamic body acceleration estimation based on inertial and magnetic sensors: Bio-logging application. Sens J IEEE 11: 233–244. doi:10.1109/JSEN.2010.2053353.
[23]  Shepard E, Wilson R, Quintana F, Laich A, Liebsch N et?al.. (2008) Identification of animal movement patterns using tri-axial accelerometry. Endangered Species Res 10: 2–1.
[24]  Whitney N, Pratt H?Jr, Pratt T, Carrier J (2010) Identifying shark mating behaviour using three-dimensional acceleration loggers. Endangered Species Res 10: 71–82. doi:10.3354/esr00247.
[25]  Bidder OR, Soresina M, Shepard EL, Halsey LG, Quintana F et?al.. (2012) The need for speed: testing acceleration for estimating animal travel rates in terrestrial dead-reckoning systems. Zoology 115: 58–64. doi:10.1016/j.zool.2011.09.003. PubMed: 22244455.
[26]  Preston T, Baltzer W, Trost S (2012) Accelerometer validity and placement for detection of changes in physical activity in dogs under controlled conditions on a treadmill. Res Vet Sci 93: 412–416. doi:10.1016/j.rvsc.2011.08.005. PubMed: 21924751.
[27]  Gleiss A, Norman B, Liebsch N, Francis C, Wilson R (2009) A new prospect for tagging large free-swimming sharks with motion-sensitive data-loggers. Fish Res 97: 11–16. doi:10.1016/j.fishres.2008.12.012.
[28]  Halsey L, Portugal S, Smith J, Murn C, Wilson R (2009) Recording raptor behavior on the wing via accelerometry. J Field Ornithol 80: 171–177. doi:10.1111/j.1557-9263.2009.00219.x.
[29]  Shamoun-Baranes J, Bom R, van Loon EE, Ens BJ, Oosterbeek K et?al.. (2012) From sensor data to animal behaviour: An oystercatcher example. PLOS ONE 7: e37997. doi:10.1371/journal.pone.0037997. PubMed: 22693586.
[30]  Gleiss A, Wilson R, Shepard E (2010) Making overall dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure. Methods Ecol Evolution 2: 23–33.
[31]  Sato K, Daunt F, Watanuki Y, Takahashi A, Wanless S (2008) A new method to quantify prey acquisition in diving seabirds using wing stroke frequency. J Exp Biol 211: 58–65. doi:10.1242/jeb.009811. PubMed: 18083733.
[32]  Fourati H, Manamanni N, Afilal L, Handrich Y (2011) Posture and body acceleration tracking by inertial and magnetic sensing: Application in behavioral analysis of free-ranging animals. Biomedical Signal Process Control 6: 94–104. doi:10.1016/j.bspc.2010.06.004.
[33]  Stiles E, Palestrini C, Beauchamp G, Frank D (2011) Physiological and behavioral effects of dextroamphetamine on beagle dogs. J Veterinary Behavior Clinical Applications Res 6: 328–336. doi:10.1016/j.jveb.2011.03.001.
[34]  Hansen BD, Lascelles BD, Keene BW, Adams AK, Thomson AE (2007) Evaluation of an accelerometer for at-home monitoring of spontaneous activity in dogs. Am J Vet Res 68: 468–475. doi:10.2460/ajvr.68.5.468. PubMed: 17472445.
[35]  Brown DC, Michel KE, Love M, Dow C (2010) Evaluation of the effect of signalment and body conformation on activity monitoring in companion dogs. Am J Vet Res 71: 322–325. doi:10.2460/ajvr.71.3.322. PubMed: 20187834.
[36]  Michel KE, Brown DC (2011) Determination and application of cut points for accelerometer-based activity counts of activities with differing intensity in pet dogs. Am J Vet Res 72: 866–870. doi:10.2460/ajvr.72.7.866. PubMed: 21728845.
[37]  Barthélémy I, Barrey E, Thibaud JL, Uriarte A, Voit T et?al.. (2009) Gait analysis using accelerometry in dystrophin-deficient dogs. Neuromuscul Disord 19: 788–796. doi:10.1016/j.nmd.2009.07.014. PubMed: 19800232.
[38]  Gillette RL, Angle TC (2008) Recent developments in canine locomotor analysis: a review. Vet J 178: 165–176. doi:10.1016/j.tvjl.2008.01.009. PubMed: 18406641.
[39]  Ribeiro C, Ferworn A, Denko M, Tran J, Mawson C (2008) Wireless estimation of canine pose for search and rescue. System Systems Eng: 2008. SoSE’08. IEEE International . Conference on. IEEE , pp. 1–6.
[40]  Gps/ins logger website. Available: . https://hal.elte.hu/flocking/wiki/public?/en/projects/ProjectHighPrecisionTrackin?gOfAnimals . Accessed 2013 Sept 17.
[41]  Nagy M, Vásárhelyi G, Pettit B, Roberts-Mariani I, Vicsek T et?al.. (2013) Context-dependent hierarchies in pigeons. Proc Natl Acad Sci USA ( published online ahead of print). doi:10.1073/pnas.1305552110. PubMed: 23878247.
[42]  Xsens mti-g website. Available: http://www.xsens.com/en/general/mti-g-10?0-series. Accessed 2013 September 17.
[43]  Subtitle Workshop website. Available: . http://www.urusoft.net/products.php?cat=?sw . Accessed 2013 Sept 17.
[44]  Chang C, Lin C (2011) Libsvm: a library for support vector machines. ACM Transactions Intell Systems Technol (TIST) 2: 27.
[45]  Chen YW, Lin CJ (2006) Combining svms with various feature selection strategies. In: Feature Extraction. Springer Verlag. pp. 315–324.
[46]  Kavanagh JJ, Menz HB (2008) Accelerometry: a technique for quantifying movement patterns during walking. Gait Posture 28: 1–15. doi:10.1016/S0966-6362(08)70001-8. PubMed: 18178436.
[47]  Yamada M, Tokuriki M (2000) Spontaneous activities measured continuously by an accelerometer in beagle dogs housed in a cage. J Vet Med Sci /. The Japanese Society of Veterinary Science 62: 443.

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