Please use this identifier to cite or link to this item:
Title: Redundant muscular force analysis of human lower limbs during rising from a squat
Authors: Yang, YY
Wang, RC
Zhang, M 
Jin, DW
Wu, FF
Keywords: Human squat lifting
Neural control analysis
Redundant muscular force
Issue Date: 2007
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2007, v. 4561 LNCS, p. 259-267 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Muscular coordination analysis of lower limbs during rising from a squat is one of the important categories in rehabilitation engineering and gymnastic science. This paper describes an efficient biomechanical model of the human lower limb with the aim of simulating the real human rising from a squat with lifting. To understand how intermuscular control coordinates limb muscle excitations , the optimal control technique is used to solve the muscle forces sharing problem. The validity of the model is assessed comparing the calculated muscle excitations with the registered surface electromyogramm (EMG) of the muscles. The results show that synergistic muscles are build up by the neural control signals using a minimum fatigue criterion during human squat lifting, with distinct phases mat include the acceleration during the initial movement and the posture at the final specified position. Synergistic muscular groups can be used to simplify motor control, and are essential to reduce the number of controlled parameters and amount of information needing to be analyzed in the performance of any motor act.
Description: 1st International Conference on Digital Human Modeling, ICDHM 2007, Beijing, 22-27 July 2007
ISBN: 9783540733188
ISSN: 0302-9743
EISSN: 1611-3349
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Citations as of Dec 9, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.