Joint motion pattern classification by cluster analysis of kinematic, demographic, and subjective variables.
The purpose of this study is to identify joint motion patterns by classifying the full range of motion (ROM) into several sections. Forty participants were stratified by age and gender and they performed 18 full-swing motions at a self-selected speed. Joint angle, angular velocity, angular acceleration, and subjective discomfort rating were collected for each motion. K-means cluster analyses were used to classify joint motion patterns and ROM sections. The results showed that two or three clusters were mainly determined by the kinematic variables of angular velocity and acceleration. The motions of three clusters showed that the ROM sections of low and moderate velocity with moderate and high accelerations occurred in the initial (negative) and terminal (positive) phases, respectively, whereas those of high velocity with low acceleration were shown in the mid (neutral) phase. The motions of two clusters revealed that while the patterns of high velocity and high acceleration were found on the positive side of the ROM, those of low velocity and low acceleration were on the negative and neutral sides. The ROM sections close to both ends of the ROM may have a larger physical load than the others. This study provides information that could be useful for developing postural analysis tools for dynamic work.
Department of Industrial Engineering, Ajou University, Suwon, South Korea.
Jaejin
J
Hwang
Hyunjung
H
Shin
Myung-Chul
MC
Jung
D000054:Acceleration; D000328:Adult; D000704:Analysis of Variance; D000886:Anthropometry / Q000379:methods; D001696:Biomechanical Phenomena; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007596:Joints / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D010147:Pain Measurement; D016059:Range of Motion, Articular / Q000502:physiology*; D012399:Rotation; D012984:Software