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

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 presented in Figure 5. The columns represent all the considered features extraction methods while the rows depicts the different combination of window lengths and window increments. *Symmetry* **2020**, *12*, x FOR PEER REVIEW 11 of 20 presented in Figure 5. The columns represent all the considered features extraction methods while the rows depicts the different combination of window lengths and window increments


**Figure 5.** Heatmap plot of Computation time across combinations of window lengths and increments. **Figure 5.** Heatmap plot of Computation time across combinations of window lengths and increments.

By closely analyzing the CT of each feature extraction method across different combinations of window lengths and increments (Figure 5), it could be seen that WL and TD2 features achieved the smallest CT of approximately 152.3 ms and 158.1 ms, while NTDFS and TD5AR6 features recorded relatively higher average CT of 385.1 ms and 226.9 ms, respectively. One possible explanation for the high computation time recorded by the NTDFS and TD5AR6 descriptors may be because they both consist of a combination of features, thus, leading to a correspondingly higher dimension compared By closely analyzing the CT of each feature extraction method across different combinations of window lengths and increments (Figure 5), it could be seen that WL and TD2 features achieved the smallest CT of approximately 152.3 ms and 158.1 ms, while NTDFS and TD5AR6 features recorded relatively higher average CT of 385.1 ms and 226.9 ms, respectively. One possible explanation for the high computation time recorded by the NTDFS and TD5AR6 descriptors may be because they both consist of a combination of features, thus, leading to a correspondingly higher dimension compared to the other feature extraction methods.

to the other feature extraction methods. Fundamentally, the higher the dimension of the extracted feature set, the more the computation time. In summary, it was observed that the smaller the difference between the window length and increment, the lesser the computation time, which would lead to the realization of a prosthesis controller with relatively faster response time. On the contrary, the larger the difference between the window length and its increment, the more the computation time, leading to a prosthesis controller with slower response time although it would result in higher classification performance. Hence, such tradeoff could be taking into consideration by prostheses manufacturers. Importantly, this phenomenon has rarely been reported till date and this phenomenon could be observed with the Fundamentally, the higher the dimension of the extracted feature set, the more the computation time. In summary, it was observed that the smaller the difference between the window length and increment, the lesser the computation time, which would lead to the realization of a prosthesis controller with relatively faster response time. On the contrary, the larger the difference between the window length and its increment, the more the computation time, leading to a prosthesis controller with slower response time although it would result in higher classification performance. Hence, such tradeoff could be taking into consideration by prostheses manufacturers. Importantly, this phenomenon has rarely been reported till date and this phenomenon could be observed with the other feature extraction methods investigated in this study.

In this section, the performance of the feature sets in terms of data point characterization were also examined by computing the F1-score values for both the amputees and able-bodied subjects across all the movement classes and the obtained results are presented in Tables 3 and 4 as follows using window length ranging from 150 to 300ms with 100ms increment. It could be seen from the results presented in Table 3 that the NTDFS, TD-PSD, and TD5AR6 features achieved relatively high F1-scores of approximately 0.99 ± 0.003%, 0.97 ± 0.005%, and 0.96 ± 0.005%, respectively, as against the PKF and MDF features that recorded the lowest accuracy of 0.58 ± 0.03%, and 0.72 ± 0.04%, across

other feature extraction methods investigated in this study.

subjects.

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