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Ao et al. Intell Robot 2023;3(4):495-513 I http://dx.doi.org/10.20517/ir.2023.28 Page 11 of 19

Figure 7. The Grad-CAM plot of the network output. The highlighted region in the plot indicates the region where the network is of high
importance for the input features, and the red box in the plot is our label for that region.

Figure 7 shows the results of the Grad-CAM; we find that the regions with attention are presented in a col-
umn arrangement, which also intuitively helps us to understand how much attention the network pays to the
channels. The black area indicates that the electrode channel information in this section contributes 0 to the
gesture recognition task, and not all of the electrode channel acquisition information has a positive effect on
the gesture recognition task. Tian et al. outlined the significant impact of artificial intelligence technology
on the application of sensors [24] . Acquisition devices for sEMG signals also generate redundant information
during gesture recognition tasks. Myoelectric acquisition devices have some connection to the engineering
task; they could further explore state-of-the-art methods. The red box shows the regions with a high degree
of attention. Among these 12 gesture recognitions, we found that the high attention regions of the network
for the input are mainly concentrated in columns 4, 6, 7, 8, 9, and 10 by the Grad-CAM graph. Therefore,
we mainly discuss the correlation between the forearm muscle groups corresponding to 4, 6, 7, and 8 and the
upper-limb flexors and upper-limb extensors corresponding to 9 and 10.

3.4. Muscle Synergy Analysis
The way in which the game interactions are determined determines the Shpeley value calculation process. In
order to accurately calculate the contribution value of each electrode channel to the gesture recognition task
process, it is necessary to follow a permutation. This ensures that the overall game rounds are guaranteed to
cover the interactions between all electrode channels. The combinations function of the itertools toolkit was
used to generate all combinations, each of which corresponds to the kinds of game interactions in each round.
Inputtheinformationofthecorrespondingelectrodechannelaccordingtothekindofgameinteractionineach
round. The electrodes appearing in the kind are kept input, and the information in the columns corresponding
to the electrode channels not appearing in the kind are all set to zero. The modified image is recognized, and
the prediction matrix of that input for the recognition task is taken out before the softmax layer of the network.