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A Comparative Analysis of the Motion Recognition Rate by Direction of Push-up Activity Using ELM Algorithm
Sangwoong Kim, Jaeyeong Ryu, Jiwoo Jeong, Dongyeong Kim, Youngho Chai
http://doi.org/10.5626/JOK.2023.50.12.1031
In this paper, we propose a motion recognition system for each direction of push-up activity using ELM algorithm. In the proposed system, a recognized motion consists of three parts. The first part is the process of reading motion data. In the process, the data acquired from the motion capture system is entered into the system"s memory. Then, the system extracts a feature vector from the motion data. The 3D position data converted from the quaternion data value of the motion data is projected onto the X-Y plane, Y-Z plane and Z-X plane of the system, and the values are used as the final feature vector. Feature vectors projected on each plane train different ELM, and a total of three ELM are learned. Finally, by inputting test data to each learned ELM, the final recognition result value is derived. First, before obtaining motion data, as the data set to be trained, general push-ups performed in the correct posture were selected. Second, the upper chest did not go down all the way. Third, only the buttocks came up when bending and lifting. Four, when bending your elbows move away from your upper chest. Finally, mix these motions to build a test dataset.
A Dynamic Gesture Recognition System based on Trajectory Data of the Motion-sphere
Jaeyeong Ryu, Adithya B, Ashok Kumar Patil, Youngho Chai
http://doi.org/10.5626/JOK.2021.48.7.781
Recently, dynamic gesture recognition technology, which belongs to human-computer interaction (HCI), has received much attention. This is because the interface configuration for utilizing the system is simple and it is possible to communicate quickly. In this paper, we used a new input data format for the dynamic gesture recognition system and conducted research to improve the recognition accuracy. In the existing dynamic gesture recognition system, the position data and the rotation data of the joint are mainly used. In the proposed system, motion-sphere trajectory data are used. Motion-sphere expresses motion intuitively as a technique for visualizing movement. In the motion-sphere, the expression is composed of the trajectory and twist angle. In this paper, the trajectory of the motion-sphere is used as input data of the dynamic gesture recognition system. The validity of the trajectory data used is verified through the dynamic gesture recognition accuracy comparison. In the experiment, we experimented on two cases. The first cases were conducted by using measured quaternion data. The other experiments used open motion data. Both experiments conducted cognitive accuracy tests, and each experiment yielded high cognitive accuracy.
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