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Micromachines 2018, 9(8), 411; https://doi.org/10.3390/mi9080411

Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology

Department of Control and Instrumentation Engineering, Chosun University, 375 Seosuk-dong, Gwangju 501-759, Korea
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Received: 17 July 2018 / Revised: 9 August 2018 / Accepted: 12 August 2018 / Published: 17 August 2018
(This article belongs to the Special Issue MEMS Accelerometers)
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Abstract

This paper discusses the classification of horse gaits for self-coaching using an ensemble stacked auto-encoder (ESAE) based on wavelet packets from the motion data of the horse rider. For this purpose, we built an ESAE and used probability values at the end of the softmax classifier. First, we initialized variables such as hidden nodes, weight, and max epoch using the options of the auto-encoder (AE). Second, the ESAE model is trained by feedforward, back propagation, and gradient calculation. Next, the parameters are updated by a gradient descent mechanism as new parameters. Finally, once the error value is satisfied, the algorithm terminates. The experiments were performed to classify horse gaits for self-coaching. We constructed the motion data of a horse rider. For the experiment, an expert horse rider of the national team wore a suit containing 16 inertial sensors based on a wireless network. To improve and quantify the performance of the classification, we used three methods (wavelet packet, statistical value, and ensemble model), as well as cross entropy with mean squared error. The experimental results revealed that the proposed method showed good performance when compared with conventional algorithms such as the support vector machine (SVM). View Full-Text
Keywords: motion analysis; auto-encoder; dance classification; deep learning; self-coaching; wavelet packet; classification of horse gaits motion analysis; auto-encoder; dance classification; deep learning; self-coaching; wavelet packet; classification of horse gaits
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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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Lee, J.-N.; Byeon, Y.-H.; Kwak, K.-C. Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology. Micromachines 2018, 9, 411.

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