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distinct movements without requiring prior knowledge to define standard eigenvalues, as needed by previous
muscle synergy analysis methods. Our method offers a promising solution for muscle synergy analysis in
human-machine interaction, with potential applications in fields such as rehabilitation and sports training. In
addition, this method can be applied not only to analyze forearm muscle interactions but also in lower limb
rehabilitation analysis, gait analysis, human movement recognition, and many other areas. As an example, we
have introduced our method for muscle synergy analysis in the context of lower limb rehabilitation. Specif-
ically, it is capable of outputting correlation analysis on lower limb muscles while recognizing the patient’s
movement intention. This enables us to evaluate the patient’s lower limb rehabilitation progress.

In the future, we plan to explore the levels of intra-variability (within an individual) and inter-variability (be-
tween individuals), and we plan to conduct an analysis of the effects of various sizes of sEMG images to assess
the correlation of synergistic relationships for similar movements. In addition, we aim to incorporate muscle
forces and joint angles to analyze changes in muscle synergy during continuous movements. For graphs, each
data sample in the graph will have edges associated with other real data samples in the graph, and it is this
information that we use to capture the inter-dependencies between muscles. Inspired by Liu et al.’s Graph
structure learning based on evolutionary computation [26] , graph neural networks are introduced to explore
the correlation between the data sample points in the sEMG image. Regarding the multi-sensor data fusion
model [27] , the first step is to determine the data type that will be used. We believe that the sEMG physiological
signal is a better choice. When using computer vision for gesture recognition, occlusion can interfere with the
recognition task. However, if our method is fused, the sEMG physiological signal can also be used as a feature
needed for the network, which may assist in addressing this problem. The design and development of sEMG
signal acquisition devices in accordance with practical engineering applications are also worthy of in-depth
investigation with respect to the large amount of redundant information derived from the interpretable new
analyses. In addition, the large amount of redundant information derived from the new analyses can be inter-
preted. The design and development of sEMG signal acquisition devices and control of intelligent prosthetics
in accordance with practical engineering applications are also worthy of in-depth investigation.

DECLARATIONS
Authors’ contributions
Made substantial contributions to the conception and design of the study and performed data analysis and
interpretation: Ao X, Wang F, Wang R
Provided administrative, technical, and material support: She J

Availability of data and materials
Not applicable.

Financial support and sponsorship
This work was supported by the National Natural Science Foundation of China under Grant 62106240; the
Natural Science Foundation of Hubei Province, China, under Grant 2020CFA031; China Postdoctoral Sci-
ence Foundation under Grant 2022M722943; Wuhan Applied Foundational Frontier Project under Grant
2020010601012175; the 111 Project under Grant B17040; and JSPS (Japan Society for the Promotion of Sci-
ence) KAKENHI under Grants 20H04566 and 22H03998.

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Before the initiation of data acquisition in the Ninapro database, each subject was given a thorough written
and oral explanation of the experiment itself, including the associated risks; the subjects would then sign