A neuro-fuzzy method was used to estimate the torque from

these EMG signals. These collected signals for each participant corresponded to 30%, 50% and 70% of maximum voluntary flexion-extension contractions. enzalutamide SD signals along the fiber direction were used and PCA was applied for each of four muscles. After estimating the EMG amplitudes using averaged rectified value method, they were mapped to the torque signal using a neuro-fuzzy model. In this model, for each trial signal, the optimum number of rules was found and then an epoch of 17 s epoch signal were used to train the model. The proposed fuzzy model resulted in %VAF (mean ± standard deviation) =96.40 ± 3.38 for all trial signals. For the comparison, the Clancy’s nonlinear dynamic model was implemented. Using the 3rd-degree polynomial, 28th-order dynamic model, the pseudo-inverse method with the tolerance

of 5.6 × 10−3, the best performance achieved was %VAF (mean ± standard deviation) =86.99 ± 9.6. The new method improved the torque estimation results. Although the Clancy’s nonlinear method was originally applied on random excitation EMG signals, its universal nonlinear structure allows adaptation with slow-varying signal in case of isometric ramp contractions. Meanwhile, slow isometric signal decreases the nonstationary properties of the signal; thus increasing the model performance. Due to the rule-based structure of neuro-fuzzy model, interpretability is one of its advantages, and therefore the less number of rules resulted in more interpretability and generalization, but this decrease should not make the system dynamic be eliminated. The majority of cases achieved 4 or 5 optimal rules. The optimum number of fuzzy rules for

each participant was different and was depended on the percentage of MVC [Table 2]. Furthermore, the common fuzzy rules at different contraction levels were identified using the distance-based analysis. Using the similarity threshold of 25%, rule no. 4 (30% MVC) was similar with all of the rules (50% MVC) [Table 3]. In this case, the most similar rule (R4) was chosen to have a one-to-one mapping. This is, in principal, similar with “merging fuzzy rules” in a fuzzy system in which the most similar rules are merged first.[63] In the meanwhile, the similarity was confirmed subjectively by checking the resulting fuzzy rules Anacetrapib in terms of the shape of the input membership functions and their weights. However, this supervision did not change the similarity-based quantitative analysis. Since the computational complexity of using the tuned neuro-fuzzy method is low, it could be efficient for online applications, such as prosthesis control. A limitation of this work was the constant posture signal recordings and also isometric contractions in which real dynamic physiological rule-based could not be assessed.