Open AccessQUANTIFICATION OF MASS AND CENTER-OF-MASS OF HEALTHY AND AMPUTATED SEGMENTS AS WELL AS FULL-BODY CENTER-OF-MASS OF AMPUTEES
Author(s) -
Q. H. Xu,
Xiangdong Wang,
Jingxin Liu,
Gongbing Shan
Publication year - 2020
Publication title -
journal of mechanics in medicine and biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.236
H-Index - 30
eISSN - 1793-6810
pISSN - 0219-5194
DOI - 10.1142/s021951942050044x
Subject(s) - body segment , center of mass (relativistic) , physical medicine and rehabilitation , regression analysis , biomechanics , motion analysis , simulation , computer science , mathematics , statistics , medicine , physics , anatomy , artificial intelligence , mechanics , energy–momentum relation
Quantification of segment-inertial uniqueness can provide a relevant foundation for motion analysis, biomechanical modeling and human motor skill optimization of both normal and amputated athletes. It is known that previous studies focused on quantifying Body Segment Inertial Parameters (BSIP) of non-amputated people in order to establish regression equations for calculating BSIPs. Until now, no anthropometrical study existed on quantifying BSIPs such as mass and center of mass (COM) of both non-amputated segment (NAS) and partially-amputated segment (PAS) of amputees. This study aims to fill the gap. A quantification method derived from Damavandi approach was applied to determine the mass and COM of PAS as well as full-body COM. For validating the reliability of this method, the calculated values were compared to the values measured by balance board test. Further, two anthropometrical approaches (i.e. Zheng and Zatsiorsky) for normal subjects were tested for their validity to estimatfe the mass and COM of NASs of amputees. The results reveal that Damavandi approach can also be used for reliable quantifying of mass and COM of PAS and Zatsiorsky’s approach is more reliable to quantify NAS masses and full-body COM of amputees, therefore, Damavandi approach and Zatsiorsky’s regression model are more suitable for motion analysis, biomechanical modeling and motor skill optimization of amputees.