Page 163 - Read Online

Ao et al. Intell Robot 2023;3(4):495-513 I http://dx.doi.org/10.20517/ir.2023.28 Page 13 of 19

With the resulting prediction matrix scores, we can see that for gesture 2, the lowest percentage of contribu-
tion is made by the information provided by electrode channel 12, and a higher percentage of contribution is
made by the information provided by electrode channels 4 to 11. We place the complete calculation results
of experiments 2-12 in the Appendix. We can obtain the highest contribution value channel and the lowest
contribution value channel for each gesture recognition from the above single-channel prediction score.

3.4.2 Synergy analysis between single channels
We calculate the synergy between muscles and different gestures using game interactions. To achieve this, the
six electrodes selected by Grad-CAM are used as game members for 64 rounds of game interactions. We then
obtain the marginal contribution of the Shapley value for the six channels. The 64 types of game interactions
are obtained by permuting the six participating members. For example, the first round involves only member
1 (channel 1) participating in the recognition task, while the second round involves only member 2 (channel 2)
participating, and so on. The 64th round involves all six members (channels) participating in the recognition
task together. To calculate the contribution of each channel to the recognition task, we require 64 scores.
We use the judgment score of the network before softmax as the reward score for that round for a certain
identified gesture. For the results of the above single-channel experiment, we focused on channels 4, 6, 7, 8,
9, and 10 to quantify the correlation between the muscles corresponding to the acquisition electrodes of these
channels in this gesture recognition experiment. First, channels 4, 6, 7, and 8 are placed equidistantly around
the anterior wall, and the muscle correlations corresponding to these electrodes are quantitatively analyzed.
With the additional score matrix, we quantified the effect of the network on the joint action of channel 4 and
channel 6. For gestures 8, 9, 10, and 11, the additional interaction of channel 4 and channel 6 is negative.
That is, the simultaneous input of channel 4 and channel 6 interferes with the network’s judgment of these
four gestures. Further, we infer that the network believes that there is a mutual inhibition of the muscles
corresponding to channel 4 and channel 6 during the performance of these four gestures, and this inhibition
interferes with the recognition accuracy of networks for these gestures. However, none of the additional scores
are high, indicating that the muscle interaction corresponding to channel electrode 4 and channel electrode 6
is relatively weak.

Followingthemethod describedabove, weobtained theone-by-onerelationshipbetween channel4, channel 6,
channel 7, channel 8, channel 9, and channel 10 corresponding to the muscles in performing these 12 gestures
for recognition. We found that the network perceives different interactions between muscles when performing
different gestures and that the joint action between certain muscles and muscles when performing certain
recognition facilitates the network’s recognition of various gestures. However, this effect is not always positive,
and when performing other gestures, certain muscle-muscle interactions inhibit the network’s judgments of
those gestures. When we performed a synergy analysis between single channels corresponding to muscles, we
found that the interactions between channel 6 and channel 8 were mostly biased towards the negative side. The
additional fraction matrix for channel 6 and 8 cooperation is shown below.

[ ]
−27.09 −14.04 52.88 52.77 −2.45 −82.35
67.36 −102.98 −46.14 −7.29 4.38 131.15

The simultaneous input of information from channel 6 and channel 8 negatively affects the network in recog-
nizing gestures 1, 2, 5, 6, 8, 9, and 10. The interaction of information from these two channels has a greater
negative impact on the recognition of gesture 6 and gesture 8. But for the recognition of gesture 12, the infor-
mation from the interaction of these two channels will have a greater positive impact.