Arjunan Sridhar P, Kumar Dinesh K, Naik Ganesh R
School of Electrical and Computer Engineering, RMIT university, Melbourne, VIC 3001, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4614-7. doi: 10.1109/IEMBS.2010.5626468.
This research paper reports an experimental study on identification of the changes in fractal properties of surface Electromyogram (sEMG) with the changes in the force levels during low-level finger flexions. In the previous study, the authors have identified a novel fractal feature, Maximum fractal length (MFL) as a measure of strength of low-level contractions and has used this feature to identify various wrist and finger movements. This study has tested the relationship between the MFL and force of contraction. The results suggest that changes in MFL is correlated with the changes in contraction levels (20%, 50% and 80% maximum voluntary contraction (MVC)) during low-level muscle activation such as finger flexions. From the statistical analysis and by visualisation using box-plot, it is observed that MFL (p ≈ 0.001) is a more correlated to force of contraction compared to RMS (p≈0.05), even when the muscle contraction is less than 50% MVC during low-level finger flexions. This work has established that this fractal feature will be useful in providing information about changes in levels of force during low-level finger movements for prosthetic control or human computer interface.
本研究报告了一项关于在低强度手指弯曲过程中，随着力水平变化识别表面肌电图（sEMG）分形特性变化的实验研究。在先前的研究中，作者已经识别出一种新的分形特征——最大分形长度（MFL），作为低强度收缩强度的一种度量，并使用该特征来识别各种手腕和手指运动。本研究测试了MFL与收缩力之间的关系。结果表明，在诸如手指弯曲等低强度肌肉激活过程中，MFL的变化与收缩水平（最大自主收缩（MVC）的20%、50%和80%）的变化相关。通过统计分析和使用箱线图进行可视化观察发现，即使在低强度手指弯曲过程中肌肉收缩小于50%MVC时，MFL（p≈0.001）与收缩力的相关性也比均方根（RMS）（p≈0.05）更高。这项工作已经证实，这种分形特征将有助于为假肢控制或人机交互提供关于低强度手指运动过程中力水平变化的信息。
