Extended user study (seven classes)
supination
For the purpose of investigating the suitability of the developed methods when including even more gestures in the training, further experiments were conducted as an extension of the described user study. Four subjects who had no EMG experience before but participated in the basic user study were selected again (subjects S5, S7, S9 and S10). On the one hand, the previous participation in the main part of the study might have influenced the impartiality. On the other hand, this might give interesting insights in the algorithms’ performances in the case of low experience with EMG-based control.Since standard RR showed to not perform well in the main part of the study, this was excluded in the extended evaluation in order to avoid participants’ demotivation. Instead, the wrist rotation gestures pronation and supination were added.Extended user study: success rates (over subjects) and significance from ANOVA (Extended user study: performance of the examined methods for individual gesture exertion levels and significance from ANOVA (For training the system, data were again gathered for 2 s per gesture at maximum sampling rate of 200 Hz with three repetitions each. This was done for all considered classes (rs, pw, pn, fl, ex, pr, su) at full-intensity exertion. This results in 8400 training sample vectors (8 channels) per person (By again repeating each task two times, the number of tasks performed per subject was 108 in total (6 gestures, 3 intensity levels, 3 learning methods, 2 repetitions). Besides these aspects, this part of the study was identical to the previous part. Again, the rest class was not explicitly tested.The results of the extended user study’s evaluation are summarized in Fig. In Fig. For each individual level, the success rates of RR-RFF and the group of kNN-based methods differ significantly (Extended user study: performance of the examined methods for individual gestures and significance from ANOVA (Extended user study: mosaic plot illustrating significantly more failures for several gestures of different exertion levels for RR-RFFThe examination of the individual gestures (see Fig. However, significant differences could be found between the methods for individual gestures: For power grasp (In Fig. As for the basic user study, the computation times needed for each classification were measured and averaged per user and method. These are presented in Table 