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Page 14 of 19 Ao et al. Intell Robot 2023;3(4):495-513 I http://dx.doi.org/10.20517/ir.2023.28

3.4.3 Synergy analysis between muscle groups
The six single electrodes were divided into three muscle groups. Specifically, electrodes 4, 6, 7, and 8 were
considered as forearm muscle groups, and the muscles corresponding to electrode 9 and electrode 10 were
considered as finger extensors and finger flexors, respectively. Discussing the three muscle groups, the number
ofgaming rounds justneeds eightrounds, and calculatingeight roundsofgaming interaction, 12 Shapley value
arrays are obtained; each array includes the marginal contribution of the corresponding electrode channels of
the three muscle groups for the gesture, and the contribution score represents the specific contribution of the
muscle group information for this recognition task. The synergistic relationship of different muscle groups for
different gestures can then be obtained. In terms of interpreting the results, we aim to address the impact of
reducing electrode information and analysis rounds on the overall performance and efficiency of our method.
To do so, we used the Grad-CAM method to cope with the large amount of data and high time complexity
of the game interaction. The Grad-CAM method reduced the redundant electrode information by locking
the ten electrode channels into six electrode channels, resulting in a 40% reduction of the raw data volume.
This reduction not only directly reduced the training time of the network but also eliminated the impact of
redundant information on the network performance. In addition, the marginal contribution of the Shapley
value to the calculation of the game interaction between electrodes is also significant. In previous research,
1024 rounds were required to obtain the synergistic relationship between different muscle groups for different
gestures. However,ourmethodreducesthenumberofelectrodesto6andconsidersthemusclescorresponding
to electrodes 4, 6, 7, and 8 placed equidistantly around the forearm as forearm muscle groups, and the muscles
corresponding to electrodes 9 and 10 as extensor and flexor muscles. Focusing on these three muscle groups,
the number of game interactions is reduced to only eight rounds to obtain the synergistic relationship between
different muscle groups for different gestures. Extend the definition of interaction to multiple variables for
muscle groups. One set is a subset belonging to . If the variables in always participate together in some
process, then the variables in form a union, which is considered as a single variable and is denoted by [ ].
The interaction in is measured by

∑
(9)
([ ]) = ([ ] | ) − ( | )
∈
When these four channels of information are input together, there is a facilitative effect for the recognition of
some gestures and a suppressive effect for the recognition of others. However, the value of the extra score is not
high, which also proves the existence of positive and negative effects that cancel each other out. The muscles
corresponding to the four channel electrodes are actually close together, so we infer that there are different
synergies between the muscles of the small muscle groups when doing different gestures, yet this synergy does
not always allow the network to make better recognition results. We explored the synergistic relationship
between the forearm muscle groups corresponding to channels 4, 5, 7, and 8 and the upper extremity flexors
and upper extremity extensors corresponding to channels 9 and 10. We consider channels 4, 5, 7, and 8 as
an alliance and treat that alliance as a member. We then consider 9 and 10 as one member. Using that total
contribution score, subtracting the total contribution scores entered simultaneously for channels 4, 5, 7, and
8, and subtracting the total contribution scores entered simultaneously for channels 9 and 10, we obtain the
matrix of additional scores for the cooperation of these two members.

[ ]
123.38 210.06 94.60 38.69 91.17 −129.36
158.69 206.31 6.56 9.56 119.79 41.42

The interaction of these two distant muscle groups could positively contribute to the network’s recognition in
most cases and only had a greater negative effect on the recognition of gesture 6. Therefore, for the recognition
of gesture 6, the network considered the interaction generated by these two muscle groups as negative.