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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 Path Fragment Management Structure for Fast Projection Candidate Selection of the Path Prediction Algorithm
Dongwon Jeong, Sukhoon Lee, Doo-Kwon Baik
This paper proposes an enhanced projection candidate selection algorithm to improve the performance of the existing path prediction algorithm. Various user path prediction algorithms have previously been developed, but those algorithms are inappropriate for a real-time and close user path prediction environment. To resolve this issue, a new prediction algorithm has been proposed, but several problems still remain. In particular, this algorithm should be enhanced to provide much faster processing performance. The major cause of the high processing time of the previous path prediction algorithm is the high time complexity of its projection candidate selection. Therefore, this paper proposes a new path fragment management structure and an improved projection candidate selection algorithm to improve the processing speed of the existing projection candidate selection algorithm. This paper also shows the effectiveness of the algorithm herein proposed through a comparative performance evaluation.
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