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Sensors 2016, 16(5), 664; doi:10.3390/s16050664

Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Classifier from the Transformed Data Information of Inertial Sensor

1
Department of Control and Instrumentation Engineering, Chosun University, 375 Seosuk-dong, Gwangju 501-759, Korea
2
Yudo-Star Co., ltd. 415, Cheongneung-Daero, Namdong-Gu, Incheon 405-817, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 29 February 2016 / Revised: 25 April 2016 / Accepted: 3 May 2016 / Published: 10 May 2016
(This article belongs to the Section Physical Sensors)

Abstract

In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider’s hip motion from the sensor information collected by inertial sensors as feature data for the classification of a horse’s gaits. Furthermore, we develop a coaching system under both real horse riding and simulator environments and propose a method for analyzing the rider’s motion. Using the results of the analysis, the rider can be coached in the correct motion corresponding to the classified gait. To construct a motion database, the data collected from 16 inertial sensors attached to a motion capture suit worn by one of the country’s top-level horse riding experts were used. Experiments using the original motion data and the transformed motion data were conducted to evaluate the classification performance using various classifiers. The experimental results revealed that the presented FCM-NFC showed a better accuracy performance (97.5%) than a neural network classifier (NNC), naive Bayesian classifier (NBC), and radial basis function network classifier (RBFNC) for the transformed motion data. View Full-Text
Keywords: classification of horse gaits; neuro-fuzzy classifier; fuzzy c-means clustering; inertial sensor; horse riding coaching classification of horse gaits; neuro-fuzzy classifier; fuzzy c-means clustering; inertial sensor; horse riding coaching
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lee, J.-N.; Lee, M.-W.; Byeon, Y.-H.; Lee, W.-S.; Kwak, K.-C. Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Classifier from the Transformed Data Information of Inertial Sensor. Sensors 2016, 16, 664.

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