Encyclopedia of Wireless Networks

Living Edition
| Editors: Xuemin (Sherman) Shen, Xiaodong Lin, Kuan Zhang

Turning Detection in Sandbar Sharks Through Accelerometer Data

  • Benjamin D. Powell
  • Gang ZhouEmail author
  • Daniel P. Crear
  • Kevin C. Weng
  • Wouter Deconinck
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32903-1_151-1



Detecting turning using accelerometer data alone is typically impossible, but by putting the indirect “tells” associated with turning through a supervised classifier, periods of frequent turning can be observed.

Historical Background

Marine wildlife is difficult to track or observe through cameras. Instead, attachable tags, typically incorporating accelerometers or gyroscopes, are often used instead. The raw data from those tags is processed and fed into supervised classifiers, machine learning algorithms that compare the data to reference data to make predictions. Many researchers (Whitney et al., 2010; Noda et al., 2013; Brownscombe et al., 2014) have successfully used accelerometers to classify long, periodic behaviors in fish, such as swimming, coasting, and mating. However, shorter and more complex behaviors, such as turning, are not as frequently covered.

Existing methods for detecting turning require more...

This is a preview of subscription content, log in to check access.


  1. Brownscombe J, Gutoswki L, Danychuk A, Cooke S (2014) Foraging behaviour and activity of a marine benthivorous fish estimated using tri-axial accelerometer biologgers. Mar Ecol Prog Ser 505:241–251CrossRefGoogle Scholar
  2. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer PW (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357CrossRefGoogle Scholar
  3. Fida B, Bernabucci I, Bibbo D, Conforto S, Schmid M (2015) Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer. Med Eng Phys 37:705–711CrossRefGoogle Scholar
  4. Holland KN, Wetherbee BM, Lowe CG, Meyer CG (1999) Movements of tiger sharks (galeocerdo cuvier) in coastal Hawaiian waters. Mar Biol 134:665–673CrossRefGoogle Scholar
  5. Jacobstein N, Porter B (eds) (2011) 11th IEEE international conference on data miningGoogle Scholar
  6. Kleerekoper H, Gruber D, Matis J (1974) Accuracy of localization of a chemical stimulus in flowing and stagnant water by the nurse shark, ginglymostoma cirratum. J Comp Physiol 98:257–275CrossRefGoogle Scholar
  7. Martiskainen P, Järvinen M, Skön J-P, Tiirikainen J, Kolehmainen M, Mononen J (2009) Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl Anim Behav Sci 119:32–38CrossRefGoogle Scholar
  8. Noda T, Kawabata Y, Arai N, Mitamura H, Watanabe S (2013) Animal-mounted gyroscope/accelerometer/magnetometer: in situ measurement of the movement performance of fast-start behaviour in fish. J Exper Mar Biol Ecol 451:55–68CrossRefGoogle Scholar
  9. Whitney NM, Pratt HLJ, Pratt TC, Carrier JC (2010) Identifying shark mating behaviour using three-dimensional acceleration loggers. Endanger Species Res 10:71–82CrossRefGoogle Scholar
  10. Wilga CD (2009) Hyoid and pharyngeal arch function during ventilation and feeding in elasmobranchs: conservation and modification in function. J Appl Icthyology 26:162–166CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Benjamin D. Powell
    • 1
  • Gang Zhou
    • 1
    Email author
  • Daniel P. Crear
    • 2
  • Kevin C. Weng
    • 2
  • Wouter Deconinck
    • 3
  1. 1.Computer ScienceCollege of William and MaryWilliamsburgUSA
  2. 2.Virginia Institute of Marine ScienceCollege of William & MaryGloucester PointUSA
  3. 3.PhysicsCollege of William and MaryWilliamsburgUSA

Section editors and affiliations

  • Honggang Wang
    • 1
  1. 1.Department of Electrical EngineeringUniversity of Massachusetts DartmouthNorth DartmouthUSA