Basic user study (five classes)
The subjects provided informed consent and statistical information as follows:Age range from 21 to 34 (mean 25, median 24).Three female and 9 male.One left-handed and 11 right-handed.Four already participated in many EMG experiments, 3 in a few and 5 without any EMG experience.In the prediction and test phase, they were asked to follow the stimulus again, in a total of 96 tasks (randomized but equally distributed among the subjects: 4 gestures (the rest class was not tested), 3 exertion levels, 4 methods, 2 repetitions) with breaks after a quarter, the half and three quarters of tasks. In this phase, they furthermore saw the prediction of the currently exerted gesture in a second hand model. The goal was to match the stimulus and the predicted gesture within some spatial margin and time frame. Success was signalled by a green visualization. Otherwise, a yellow visualization was shown as visual feedback.Basic user study: performance of the examined methods for individual gestures and significance from ANOVA (The summarized performance of each examined method for the 12 subjects is depicted in Fig. Figure When looking into the results from the RR-based methods, it can be observed that there is no level of intensity where those would have outperformed the kNN-based methods in median and mean of the achieved success rate. Interestingly, standard RR yielded a higher number of successes for low-intensity signal amplitudes than when incorporating random Fourier features. In contrast, RR-RFF performed better than RR for gestures of full intensity. For gestures of Basic user study: mosaic plot illustrating significantly more successes for several gestures of different exertion levels for kNN-based methods with significantly more failures in certain cases for RR-based methodsAt the level of The relation between individual gestures and success rate is presented in Fig. Wrist flexion was the second best detected gesture for the kNN-based methods (about 76% mean for both), followed by power grasp (67% median), and concluded by the pointing gesture with the worst performance (about 55% mean).For both kNN and DSM-kNN, the performance difference between wrist extension and pointing was significant. For DSM-kNN, the comparison of wrist extension and power grasp also yielded significance.While RR exposed the same tendency of gesture performances as the kNN-based methods (on a lower baseline), for RR-RFF, the pointing gesture yielded the best success rate on average (median 58%, mean 51%). Interestingly, wrist extension exposed the worst performance of gestures for RR-RFF (33% median, 31% mean). Wrist flexion and power grasp revealed the same tendency as described for the other methods. For the RR-based methods, no significance could be shown between different gestures.With regard to the individual gestures, the group of kNN-based methods performed significantly better than the RR-group for power grasp (The overall relations are summarized in Fig. In Table Basic user study (5 classes, 12 subjects), online classification time per subject and method, averaged for all prediction samples, confirming real-time performance