*3.1. Analysis of the Feature Sets Based on Classification Error across Windowing Parameters*

In this section, the properties of the different extracted feature sets were studied in terms of their classification error (CE) across combinations of window lengths and increments (Table 1) for movement intent decoding based on the LDA algorithm. The obtained results across subjects and movement classes is presented using the Heatmap plot shown in Figure 4. This analysis shows the average CE across subjects (amputees and able-bodied subjects) and movement classes, where the columns represent the different extracted features and the rows denote the combinations of windowing parameters utilized in this study. *Symmetry* **2020**, *12*, x FOR PEER REVIEW 10 of 20 columns represent the different extracted features and the rows denote the combinations of windowing parameters utilized in this study.



**Figure 4.** Heatmap plot of classification error across combinations of window lengths and increments. **Figure 4.** Heatmap plot of classification error across combinations of window lengths and increments.

It is worth noting that preliminary analysis showed that the symmetrical movement intent elicitation experiment protocol (Figure 2a) adopted in this study was helpful in aiding the amputee subjects perform seven of the pre-defined classes of movements with their amputated limbs. Meanwhile, all the results presented in this study are based on the recordings from the amputated limb, and not both It is worth noting that preliminary analysis showed that the symmetrical movement intent elicitation experiment protocol (Figure 2a) adopted in this study was helpful in aiding the amputee subjects perform seven of the pre-defined classes of movements with their amputated limbs. Meanwhile, all the results presented in this study are based on the recordings from the amputated limb, and not both limbs.

limbs. From Figure 4, it could be observed that the PKF and MDF features achieved the lowest classification performances with an average CE value of 45.23 ± 4.95% and 26.95 ± 2.33% while the NTDFS, TD-PSD, and TD5AR6 recorded an average CE of 1.07 ± 0.35%, 3.52 ± 0.83%, and 4.70 ± 1.08%, From Figure 4, it could be observed that the PKF and MDF features achieved the lowest classification performances with an average CE value of 45.23 ± 4.95% and 26.95 ± 2.33% while the NTDFS, TD-PSD, and TD5AR6 recorded an average CE of 1.07 ± 0.35%, 3.52 ± 0.83%, and 4.70 ± 1.08%, respectively, which were much better than the other feature extracted methods.

respectively, which were much better than the other feature extracted methods. In summary, by critically analyzing the results shown in Figure 4, it was found that keeping the window length constant and varying the increment parameter does not meaningfully influence the classification performance of the extracted feature sets. On the other hand, varying the window length with a relatively constant increment would have more influence on the classification performance of the extracted feature sets. For instance, considering the RMS feature, when the In summary, by critically analyzing the results shown in Figure 4, it was found that keeping the window length constant and varying the increment parameter does not meaningfully influence the classification performance of the extracted feature sets. On the other hand, varying the window length with a relatively constant increment would have more influence on the classification performance of the extracted feature sets. For instance, considering the RMS feature, when the increment parameter

when the window length was kept constant at 200 ms, average decoding errors of 11.46%, 11.50%, 11.60%, 11.55% were obtained for 50 ms, 75 ms, 100 ms, and 125 ms increments, respectively. Overall it could be deduced that most features achieved the least CE at window increment of 100 ms, hence

In this section, the characteristics of the feature sets were further studied based on their computation time (CT) across different combination of window lengths and increments, and the analysis was done based on sEMG data from both category of subjects, as shown in the Heatmap plot

the subsequent analysis were conducted using a window increment of 100 ms.

*3.2. Analysis of the Feature Sets Based on Computation Time across Windowing Parameters* 

is fixed at 50ms, average decoding errors of 14.05%, 12.34%, 11.46%, and 10.35% were recorded for 150 ms, 200 ms, 250 ms, and 300 ms window lengths, respectively. Meanwhile, when the window length was kept constant at 200 ms, average decoding errors of 11.46%, 11.50%, 11.60%, 11.55% were obtained for 50 ms, 75 ms, 100 ms, and 125 ms increments, respectively. Overall it could be deduced that most features achieved the least CE at window increment of 100 ms, hence the subsequent analysis were conducted using a window increment of 100 ms.
