Department of Industrial, Electronics and Mechanical Engineering, Roma Tre University, 00154 Roma, Italy.
Department of Engineering, University of Messina, 98158 Messina, Italy.
Sensors (Basel). 2022 May 24;22(11):3972. doi: 10.3390/s22113972.
The estimation of the sEMG-force relationship is an open problem in the scientific literature; current methods show different limitations and can achieve good performance only on limited scenarios, failing to identify a general solution to the optimization of this kind of analysis. In this work, this relationship has been estimated on two different datasets related to isometric force-tracking experiments by calculating the sEMG amplitude using different fixed-time constant moving-window filters, as well as an adaptive time-varying algorithm. Results show how the adaptive methods might be the most appropriate choice for the estimation of the correlation between the sEMG signal and the force time course. Moreover, the comparison between adaptive and standard filters highlights how the time constants exploited in the estimation strategy is not the only influence factor on this kind of analysis; a time-varying approach is able to constantly capture more information with respect to fixed stationary approaches with comparable window lengths.
肌电信号-力关系的估计是科学文献中的一个开放性问题;目前的方法显示出不同的局限性,并且只能在有限的场景下取得良好的性能,无法为这种分析的优化找到一个通用的解决方案。在这项工作中,我们通过使用不同的固定时间常数移动窗口滤波器以及自适应时变算法来计算肌电幅度,从而在两个与等长力跟踪实验相关的不同数据集上估计了这种关系。结果表明,对于肌电信号与力时程之间的相关性估计,自适应方法可能是最合适的选择。此外,自适应滤波器与标准滤波器之间的比较表明,在估计策略中使用的时间常数并不是这种分析的唯一影响因素;与具有可比窗口长度的固定静止方法相比,时变方法能够不断捕获更多的信息。