3D motion analysis of national rider athletes by riding types in horse simulator

Lee, Myung-Won; Kwak, Keun-Chang

doi:10.1109/intech.2013.6653666

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…In the same manner as for real riding, a database was constructed from data collected from a motion capture suit to which 16 inertial sensors were attached worn by the country’s top-level horse riding expert. As shown in Figure 8 b, the coaching system compares the expert’s motion with the user’s motion and informs the user of the correct riding motion corresponding to the classified riding gait through text and speech on the basis of the motion analysis as mentioned above [ 30 ].…”

Section: Construction Of Horse Rider’s Motion Databasementioning

confidence: 99%

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

Lee

Byeon

et al. 2016

Sensors

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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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Section: Construction Of Horse Rider’s Motion Databasementioning

confidence: 99%