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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 15 of 19

Figure 8. Muscle synergy analysis using non-negative matrix decomposition [25] .

3.4.4 Comparison with previous work
In this section, we compare the SVMS method with the classical muscle synergy analysis method. Teng et
al. utilized synergy for gesture recognition methods with integrity [25] . In their approach, the first step is to
model muscle synergy analysis, where high-dimensional MAP (muscle activation patterns) can be represented
as linear combinations of low-dimensional muscle synergies activated by corresponding activation coefficients.
In the second step, MAP approximations need to be established. sEMG data are extracted through analysis
windows, and features are extracted from each analysis window to form a feature matrix for each gesture.
This matrix is known as the MAP matrix. Typical TD features, i.e., RMS and mean absolute value (MAV),
were used to generate the MAP matrix. In the third step, muscle synergies were extracted and estimated
from the MAP matrix using non-negative matrix decomposition, but the number of synergies needed to be
determined manually. The non-negative matrix decomposition algorithm generates multiple solutions, and
the set of muscle synergies that minimizes the error needs to be extracted for data reconstruction. In addition,
the coefficient of determination (R2) is needed as a criterion. The reconstruction superiority of the raw sEMG
signal data is measured by the estimated muscle synergies and the corresponding activation coefficients.

For feature selection, the SVMS method is performed based on CNNs, avoiding the step of manual feature
extraction. For the analysis window, the SVMS method converts the signal into an image for processing and
utilizesaninterpretablemethodtofiltertheinformationthatisimportantfortherecognitiontask, avoidingthe
establishment and processing of the feature matrix. In synergy analysis, the SVMS method utilizes the game
interaction theory to establish the interaction between information. The manual determination of the number
of synergies is avoided. Furthermore, it does not utilize non-negative matrix decomposition to estimate the
muscle synergy from the MAP matrix. There is no need to consider the problem that the non-negative matrix
decomposition algorithm generates multiple solutions.

4. CONCLUSIONS
In this paper, we introduce a novel and effective method for muscle synergy analysis, named SVMS, which
is both interpretable and has end-to-end functionality. The SVMS method employs a time-domain sliding
window for data acquisition, achieving a high gesture recognition accuracy of 94.26%. We analyze the syn-
ergy between the muscles associated with twelve different gestures and demonstrate the effectiveness of our
method. The advantage of the SVMS method is that it explores the correlation between muscles involved in