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Home / Equine Research Bank / Sensors (Basel) / Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Cl...
Sensors (Basel, Switzerland)2016; 16(5); doi: 10.3390/s16050664

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

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.
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Publication Date: 2016-05-10 PubMed ID: 27171098PubMed Central: PMC4883355DOI: 10.3390/s16050664Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research uses a neuro-fuzzy classifier and a wavelet packet to analyze and classify four different gaits (ways of moving) in three different breeds of horses. The horses’ movements are recorded using sensors, and the data from these sensors is used to determine the horse’s gait and teach horse riders the correct corresponding motion.

Objective and Method

  • This research primarily aims to accurately classify four different gaits of horses (walk, sitting trot, rising trot, and canter) using a neuro-fuzzy classifier. This classifier is designed using a fuzzy c-means clustering algorithm, which can effectively handle the problem of increased dimensionality due to the flexible partitioning of scatter data.
  • The movements of the horse are recorded using 16 inertial sensors attached to a suit worn by a top-level horse riding expert. This motion data is then transformed by a wavelet packet to be compatible with the classifier.

Development of Coaching System

  • Apart from classifying gaits, the research also aims to develop a coaching system for equestrian riders. The coaching system uses the recorded inertial sensor data from the horse. The coaching system is designed to work in both real horse riding environments as well as simulator environments.
  • The proposed coaching system further proposes a method of analyzing a rider’s movement and motion in alignment with the classified gait of the horse. This can assist riders in achieving the correct motion when they’re riding a horse exhibiting a specific gait.

Results and Findings

  • Experiments were conducted to evaluate the performance of the classification system by comparing it to other classifiers like the neural network classifier (NNC), naive Bayesian classifier (NBC), and the radial basis function network classifier (RBFNC).
  • The findings of the experiment show that the new classification process (called FCM-NFC) is superior with a high accuracy rate of 97.5%, outperforming other classifiers when applied to the transformed motion data.

Cite This Article

APA
Lee JN, Lee MW, Byeon YH, Lee WS, Kwak KC. (2016). Classification of Horse Gaits Using FCM-Based Neuro-Fuzzy Classifier from the Transformed Data Information of Inertial Sensor. Sensors (Basel), 16(5). https://doi.org/10.3390/s16050664

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 16
Issue: 5

Researcher Affiliations

Lee, Jae-Neung
  • Department of Control and Instrumentation Engineering, Chosun University, 375 Seosuk-dong, Gwangju 501-759, Korea. ljn1321@daum.net.
Lee, Myung-Won
  • Department of Control and Instrumentation Engineering, Chosun University, 375 Seosuk-dong, Gwangju 501-759, Korea. mailsanai@daum.net.
Byeon, Yeong-Hyeon
  • Department of Control and Instrumentation Engineering, Chosun University, 375 Seosuk-dong, Gwangju 501-759, Korea. qasdfghjt@daum.net.
Lee, Won-Sik
  • Yudo-Star Co., ltd. 415, Cheongneung-Daero, Namdong-Gu, Incheon 405-817, Korea. wslee@yudostar.com.
Kwak, Keun-Chang
  • Department of Control and Instrumentation Engineering, Chosun University, 375 Seosuk-dong, Gwangju 501-759, Korea. kwak@chosun.ac.kr.

MeSH Terms

  • Algorithms
  • Animals
  • Bayes Theorem
  • Biosensing Techniques
  • Fuzzy Logic
  • Gait
  • Horses
  • Humans
  • Walking

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Citations

This article has been cited 4 times.
  1. Crecan CM, Peștean CP. Inertial Sensor Technologies-Their Role in Equine Gait Analysis, a Review. Sensors (Basel) 2023 Jul 11;23(14).
    doi: 10.3390/s23146301pubmed: 37514599google scholar: lookup
  2. Turimov Mustapoevich D, Muhamediyeva Tulkunovna D, Safarova Ulmasovna L, Primova H, Kim W. Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms. Sensors (Basel) 2023 Feb 13;23(4).
    doi: 10.3390/s23042107pubmed: 36850710google scholar: lookup
  3. Domino M, Borowska M, Zdrojkowski Ł, Jasiński T, Sikorska U, Skibniewski M, Maśko M. Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding. Sensors (Basel) 2022 Aug 13;22(16).
    doi: 10.3390/s22166052pubmed: 36015813google scholar: lookup
  4. Lee JN, Byeon YH, Kwak KC. Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology. Micromachines (Basel) 2018 Aug 17;9(8).
    doi: 10.3390/mi9080411pubmed: 30424344google scholar: lookup
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