# Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees
## 4 Discussion  
What novel insights can one draw from our analyses? First, despite showing an advantage over all in the analysis (statistical main effect of Feature Vector), we find no significant advantage of the TD-PSD feature vector over the more classical features when looking specifically at the optimal combination of log transform, look-back and majority voting is applied. This is not entirely surprising. The spectral moments \(m_{0}\), \(m_{2}\) and \(m_{4}\) in the TD-PSD vector are related to the power (RMS), zero crossings (ZC) and slope sign changes (SSC) of the Classic feature vector and both vectors include a measure of waveform length (WV and \(R_{WL}\)). The TD-PSD vector includes additional parameters related to Sparseness (\(S\)) and Irregularity factor (\(I_{irr}\)), but since these two measures are derived from \(m_{0}\), \(m_{2}\) and \(m_{4}\), they do not necessarily bring any new information that can be exploited by the classifier. The TD-PSD features may nevertheless be attractive in a practical case because they rely less on empirical constants (e.g., \(\epsilon\) thresholds) that could vary from user to user. Note nevertheless that the TD-PSD features include a \(\lambda\) factor that is also empirical (see [9]). In terms of robustness, it would presumably be better if the machine learning algorithm could find the optimal value for this parameter. With a new database in hands, we would propose to perform a grid search for different \(\lambda\) values. This could be done by methodically evaluating the accuracy changes using different \(\lambda\) values and choosing the one that outperforms over users. Second, our systematic comparison across different classifiers reveals that LDA is reliably better than the three other methods subjected to our statistical analysis (and presumably better than the others as well). One should note, however, that the SVM, BAG and RF methods are not terribly far behind. If they are easier to compute, they could be considered as well. Third, combining the present and past sample (PPa) and applying majority voting (MV) each individually improved performance, however, there is little improvement gained by combining present and past when majority voting is applied. This makes sense, as both of these methods represent a means to look further back in time. MV is another form of low-pass filtering that will reject spurious classifications when neighbouring values converge to a different categorization. Like low-pass filtering of continuous signals, however, PPa and MV will add latency to the detection of actual state changes, in the MV case depending on the number of values used in the voting. We use \(N=20\) which, for windowing at 150 ms with overlap of 50 ms, leads to a potential delay of up to 1 full second before a state change in the intention of the user is output to the prosthetic device. For the state of the art in which prostheses move slowly, this may be acceptable, but a more detailed analysis of the optimal value for \(N\) is also warranted. And as for the PPa look-back filtering, one might also ask if simply widening the time windows or low-pass filtering might also improve accuracy without the added complexity of implementing a voting scheme. Which  
Figure 6: Inter-subject variability for different filters. Smaller values indicate greater consistency in performance across individual subjects. of theseo apply will probably also depend on the ease of implementation (PPa is easier to compute than a moving majority vote) and on the desired response latency. Finally, one can consider the consistency across subjects. Consistently lower accuracy rates are obtained for one user than for all the others regardless of the methodology. Possible explanations include low remaining muscle activity, thicker fat layer, incorrect electrode location, etc.