Assessment Of Neurologic Function In Mixed Martial Arts Fighters Following A Single Competition

Munce, Thayne A.; Poel, Daniel N.

doi:10.1249/01.mss.0000681024.45945.3f

Cited by 2 publications

(1 citation statement)

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“…e marching cube algorithm is used to extract triangular meshes from the volume model and sample additional external nodes, which are used to calculate the nonrigid deformation of nonhuman parts. e time encoder is adopted because the future frame can obtain clues from the past video attitude information to evaluate the motion function [11]. After noise reduction, if a person's posture is fuzzy and ambiguous or the body is partially occluded in a given frame, the human motion posture information in the previous frame can help and restrict the subsequent posture estimation [12].…”

Section: Generate 3d Model Of Baduanjinmentioning

confidence: 99%

IoT Networks Assisted Baduanjin Auxiliary Training System Based on Depth Camera

Wan

Yong-qiang

Gao

2022

Security and Communication Networks

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Baduanjin auxiliary training system has insufficient feature definition for similar actions, which affects the recognition accuracy of effective action data. A depth camera-based Baduanjin auxiliary training system is designed. The depth camera is used as the core to design the peripheral circuit and develop the relevant firmware in the hardware part. TPS2552 is used to design the USB current limiting protection circuit, and a CrossLink chip is used to realize the interface and level conversion. In the software part, the human body 3D model of Baduanjin training is generated and the parameters of the human body model are obtained by establishing the mapping relationship between the model and the human body features in the image. Based on the automatic calibration of Baduanjin motion feature points by the depth camera, the motion posture was analyzed and summarized, the attitude auxiliary indicators were established, and the posture standard was judged by comparing the distance of the posture joints in the reconstructed 3D human body model. The system test results show that the recognition accuracy of Baduanjin auxiliary training system based on the depth camera is 96.25% for effective action data, which is 11.53%, 15.28%, and 13.06% higher than that of Kinect, motion intervention, and behavior recognition auxiliary training system, respectively. Therefore, the system can provide analysis and guidance for Baduanjin movement.

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Section: Generate 3d Model Of Baduanjinmentioning

confidence: 99%