Four-Joint Based Motion Capture for Spinal Disease Prevention System

Ko, Kyeong-Ri; Chae, Seung‐Hoon; Bae, Seong Bong; Choi, Jang Sik; Pan, Sung Bum

doi:10.1080/02564602.2014.978397

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Motion capture is a technique to measure and record the position and direction of a moving object in 3D space as data, which can be used for the computer analysis and application. It is classified into optical, mechanical, and magnetic types, per the acquisition method 6,7 . However, existing equipment has some drawbacks due to space constraints and long preparation times required for equipment installation.…”

Section: Introductionmentioning

confidence: 99%

Motion data acquisition method for motion analysis in golf

Hwang

Pan

2019

Concurrency and Computation

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As golf becomes more popular, interest in the analysis and correction of golf swing postures increases. Thus, various methods for analyzing the golf swing have been studied. Methods have recently been extended to 3D analysis systems, using motion capture technology beyond 2D analysis using cameras. The inertial sensor is one of the sensors used in 3D analysis. It is mainly used in the motion capture system, because it is cheap and has negligible space limitations. However, the motion capture system using the inertial sensor has some disadvantages in that it cannot identify body characteristics, such as joint length or the subject's initial direction, which are required to accurately analyze the swing data of the subject in golf, where the initial posture is important. This study proposes a system that obtains the same motion as the actual one by acquiring information on the motion of the subject using 15 inertial sensors and using information on the actual joint length and initial direction of the subject extracted from a depth camera. Experimental results show that the measurement error of information on the joint length and the foot stance of subjects extracted through the depth camera ranges from a minimum of 4.4% to a maximum of 6.94%. The acquired motion data from the proposed system confirms that the motion is the same as the one taken by the subject.

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Section: Introductionmentioning

confidence: 99%

Motion data acquisition method for motion analysis in golf

Hwang

Pan

2019

Concurrency and Computation

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Four-joint motion data based posture classification for immersive postural correction system

Chae

Moon

et al. 2016

Multimed Tools Appl

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Feature Extraction and Classification of Posture for Four-Joint based Human Motion Data Analysis

Ko¹,

Pan²

2015

Journal of the Institute of Electronics and Information Enginee

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In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this study, we collected users' postures and judged whether they are normal or abnormal. To obtain a user's posture, we propose a four-joint motion capture system that uses inertial sensors. The system collects the subject's postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints' rotation angles and positions; the normal posture judgment reached a success rate of 99.79%. This result suggests that the features of the four joints can be used to judge and help correct a user's posture through application to a spinal disease prevention system in the future.

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Four-Joint Based Motion Capture for Spinal Disease Prevention System

Cited by 3 publications

References 6 publications

Motion data acquisition method for motion analysis in golf

Motion data acquisition method for motion analysis in golf

Four-joint motion data based posture classification for immersive postural correction system

Feature Extraction and Classification of Posture for Four-Joint based Human Motion Data Analysis

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