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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 7 of 19

during a certain gesture recognition process. Given two variables and , ( | ) and ( | ) denote their
Shapley Values, respectively. If the variables and always participate in the process together and are always
absent together, then they can be considered to form a coalition. The reward received by the coalition is
usually different from the sum of the rewards received by the variables and when they participate in the
whole process alone. This coalition can be considered as a single variable, denoted by . In this way, we can
consider the entire process with only − 1 channels, including the variable and excluding the variables
and . The interaction between variables and is defined as an additional bonus brought by the coalition .
The extra contribution ( ) made by the cooperation of the variables and is calculated as

( )
( ) ′ [ ( )]
= | − ( | ) + | (7)

)
(
The variables and cooperate to obtain a high reward, and the interaction is positive if > 0. The
(
)
interaction between variables and leads to a low reward; the interaction is negative if < 0.
We can extend the definition of interaction to multiple variables. We define the channels that always input
informationtogetherastheset . denotesthetotalnumberofchannels. Weconsiderthechannelscontained
in as a whole and define it as a single variable and is denoted by [ ]. The interaction in is ([ ]), which
is measured as

∑
([ ]) = ([ ] | ) − ( | ) (8)
∈

By using the SVMS method, we first verify quantitatively whether there is a synergistic effect between forearm
muscles. Then, we verify the correlation between a single electrode and a small muscle block for which a
single electrode is used. Finally, we define the positively correlated muscle blocks as a coalition and explore
the synergy between muscle groups.

3. EXPERIMENT AND ANALYSIS
In this section, we first preprocess the Ninapro dataset to convert it into color images of the sEMG signals.
Then, we constructed the CNN model framework and completed the super-parameter setup. We performed
Grad-CAM on the image dataset based on the CNN framework to obtain the important feature regions of
the network for the input. Finally, we validated the synergy analysis between individual muscles and between
muscle groups in these twelve gesture recognition cases using SVMS for the Grad-CAM results.

3.1. sEMG image preprocessing
The Ninapro (Non-Invasive Adaptive Prosthetics) dataset was divided into three sub-databases according to
the acquisition procedure and subject characteristics, and we worked on the data from the first database. The
first database contains 27 intact subjects (20 males and seven females), 25 of them using the left hand and
two using the right hand, and their age was in the range of 28 ± 3.4 years. The acquisition targets sEMG
signals from 12 basic finger movements, and the acquisition device contains multiple sensors for recording
hand kinetics, kinematics, and corresponding muscle activity. Hand kinetics were measured using the finger
force linear sensor FFLS, flexion and extension forces of all fingers, and abduction and adduction of the thumb
were detected using strain gauge sensors. Ten MyoBok 13E200-50 electrodes (Otto Bock HealthCare GmbH)
provided amplified, band-pass filtered, and root mean square (RMS) corrected versions of the raw sEMG
signals corrected version of the original sEMG signal. The amplification gain of the electrodes was set to
approximately14000. Thefirsteightelectrodeswereplacedinanisometricpatharoundtheforearm. Electrode