Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg 9220, Denmark.
IEEE Trans Biomed Eng. 2012 Jul;59(7):1804-7. doi: 10.1109/TBME.2012.2197210. Epub 2012 May 2.
This letter investigates simultaneous and proportional estimation of force in 2 degree-of-freedoms (DoFs) from intramuscular electromyography (EMG). Intramuscular EMG signals from three able-bodied subjects were recorded along with isometric forces in multiple DoF from the right arm. The association between five EMG features and force profiles was modeled using an artificial neural network. Correlation coefficients between the measured and the estimated forces were 0.85 ± 0.056 and 0.88 ± 0.05 without and with post processing, respectively. The results showed that force can be estimated in 2 DoFs with high accuracy and that the degree of performance depended on the force function (task) to be estimated.
本研究旨在通过肌内电 （EMG） 信号同时且成比例地估计 2 自由度 （DoF） 下的力。对 3 名健康受试者的肌内 EMG 信号进行了记录，同时对右臂进行了多自由度的等长力测量。采用人工神经网络对 5 个 EMG 特征与力曲线之间的关系进行了建模。经后处理前后，测量力与估计力之间的相关系数分别为 0.85 ± 0.056 和 0.88 ± 0.05。结果表明，肌内 EMG 信号可以高精度地估计 2 自由度下的力，且估计性能取决于待估计的力函数 （任务）。
