Hashemi Javad, Morin Evelyn, Mousavi Parvin, Hashtrudi-Zaad Keyvan
Department of Electrical and Computer Engineering, School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:3098-101. doi: 10.1109/EMBC.2012.6346619.
A modification method based on integrated contact pressure and surface electromyogram (SEMG) recordings over the biceps brachii muscle is presented. Multi-site sEMGs are modified by pressure signals recorded at the same locations for isometric contractions. The resulting pressure times SEMG signals are significantly more correlated to the force induced at the wrist (FW), yielding SEMG-force models with superior performance in force estimation. A sensor patch, combining six SEMG and six contact pressure sensors was designed and built. SEMG, and contact pressure data over the biceps brachii and induced wrist force data were collected from 5 subjects. Polynomial fitting was used to find a mapping between biceps SEMG and wrist force. Comparison between evaluation values from models trained with modified and non-modified SEMG signals revealed a statistically significant superiority of models trained with the modified SEMG.
提出了一种基于肱二头肌上集成接触压力和表面肌电图（SEMG）记录的修正方法。多部位表面肌电图通过在相同位置记录的等长收缩压力信号进行修正。得到的压力乘以表面肌电图信号与手腕处产生的力（FW）的相关性显著更高，从而产生在力估计方面具有卓越性能的表面肌电图-力模型。设计并构建了一个结合六个表面肌电图和六个接触压力传感器的传感器贴片。从5名受试者收集了肱二头肌上的表面肌电图、接触压力数据以及手腕诱导力数据。使用多项式拟合来找到肱二头肌表面肌电图与手腕力之间的映射关系。对使用修正和未修正表面肌电图信号训练的模型的评估值进行比较，结果显示使用修正表面肌电图训练的模型具有统计学上的显著优势。
