Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis

The accuracy of predictions of muscle force based on electromyography (EMG) is an important issue in biomechanics and kinesiology. Since human skeletal muscles show a high diversity and heterogeneity in their fiber architecture, it is difficult to properly align electrodes to the muscle fiber direction. Against this background, we analyzed the effect of different bipolar configuration directions on EMG-based force estimation. In addition, we investigated whether principal component analysis (PCA) can improve this estimation. High-density surface-EMG from the triceps brachii muscle and the extension force of the elbow were measured in 11 subjects. The root mean square difference (RMSD) between predicted and measured force was determined. We found the best bipolar configuration direction to cause a 13% lower RMSD relative to the worst direction. Optimal results were obtained with electrodes aligned with the expected main muscle fiber direction. We found that PCA reduced RMSD by about 40% compared to conventional bipolar electrodes and by about 12% compared to optimally aligned multiple bipolar electrodes. Thus, PCA contributes to the accuracy of EMG-based estimation of muscle force when using a high-density EMG grid.