Quantitative relationship between needle electrode electromyography signal of AR model parameter and local muscular fatigue
- VernacularTitle:针电极肌电信号AR模型参数与局部肌疲劳的相关定性关系
- Author:
Zhi LOU
;
Jihai YANG
- Publication Type:Journal Article
- From:
Chinese Journal of Tissue Engineering Research
2006;10(30):174-176
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND: Research on muscular fatigue has extensive value of application in fundamental research about neuromuscular system, handicapped rehabilitation engineering, objective evaluation on physiotherapeutic effect, scientific training of athletes and ergonomics etc.OBJECTIVE: To study partial muscular fatigue by using AR model parameter of needle electrode electromyography (NEMG) signal and try to reveal the quantitative relationship between local muscular fatigue process and AR model coefficient of NEMG signal.DESIGN: Human NEMG signal was taken as the subject, the changing rule of myoelectric characteristics parameter in local muscular fatigue process was studied.SETTING: NML Laboratory of China University of cience and Technology.PARTICIPANTS: 4 eases of NEMG signals were obtained from NEMG signal collection system manufactured by NML Laboratory of China University of Science and Technology. Four volunteers that selected were healthy males and their tibialis anterior muscles were tested.METHODS: Based on the theory of random signal parameter model,NEMG signals were modeled and parameter was selected, the trend of parameter of NEMG signal changing with the time increasing was studied in the muscular fatigue process. Relative programs from MATLAB language toolbox were adopted for programming.MAIN OUTCOME MEASURES: The trend of α1 parameter of NEMG signal changing with the time (fatigue process) increasing. RESULTS: There was a correlation between local museular fatigue of human body and the changing trend of α1 parameter of NEMG signal tested from the muscle, that is, α1 parameter of NEMG signal increased with the time (fatigue process) increasing.CONCLUSION: By means of the increasing trend of α1 parameter of NEMG signal with the time (fatigue process) increasing, muscular fatigue state can be better evaluated.
