## 1 Introduction  
The skeletal muscles are some of the most important structures of the body; they make up more than 50% of the human body and are the ones that generate movement and help maintain its figure. These are composed of many fibers connected to various points of the bones through the tendons. To generate a movement, an electrical stimulus generates a contraction in the muscle fibers, temporarily increasing the force exerted on the tendons, and related bones. Different approaches to simulating human tissue respond to different performance and functional requirements. For example, interactivity is required for real-time applications, such as virtual surgery simulators, but visual realism is more desirable in the entertainment industry. Moreover, biomechanical accuracy is most crucial in designing medical applications. A notable example is in Computer-Assisted Surgery (CAS), where a connection to biomechanics has helped by defining a theoretical and numerical framework that provides information about the mechanics of the tissues after a clinical treatment or surgical intervention [1]. CAS has addressed a larger spectrum of clinical domains such as cardiology [2], neurosurgery [3], urology [4], and abdominal surgery [5]. For these applications, biomechanics faces a new challenge since the involved tissues are required to move and be deformed by stress generated by clinical actions. Moreover, soft tissues are difficult to model accurately since they typically exhibit complex, time dependent, non-linear, inhomogeneous and anisotropic behaviors. Such models are very computationally demanding and are therefore limited to pre-operative use, since the simulations often require many minutes or hours to compute. For clinical applications interactivity is critical, and reduced computational times are essential. The Finite element Method (FEM) is one common numerical approach used to simulate skeletal muscles [6; 7; 8; 9; 10; 11]. However, modeling of complicated 3D muscle geometries increases the complexity of mesh generation for Finite Element (FE) analysis. Poorly built meshes lead to mesh distortion and significant errors in the FE analysis. Standard FE approaches are still ineffective in handling extreme material distortions, as it could occur in muscle deformations. In 3D subject specific models, fiber direction is measured at each of the muscle pixel points, which need to be interpolated at the integration points in FE model, which introduces additional approximation errors in the FE analysis. Additionally, performance depends on the model's mesh quality and complexity, and any change to the mesh during the analysis represents an extra computational cost, which is a significant drawback. Another complex step of FEM in electromechanics is the coupling between electrophysiology and mechanics. Meshless approaches are a possible solution to address these limitations. Meshless methods [12] have several advantages over the FEM, and as such have been used to simulate a wide range of biomechanical phenomena. The work of [PERSON] et al. [13] showed that meshless methods have apparent advantages over the FEM in biomechanical problems dealing with large strains, such as in the simulation of the human lateral collateral ligament and the human knee joint. Furthermore, [PERSON] et al. [14] extended a meshless whose results confirmed the accuracy of meshless methods to deal with highly demanding nonlinear hyperelastic biomaterials. Soft tissue simulations have also benefited from the meshless formulation. [PERSON] et al. [15] presented a meshless method for computing the deformation of soft tissue. The model presented a speedup of 2 times the speed of a hexahedral-based FE simulation, and a speedup of 3 times when compared to a similar tetrahedral-based simulation. In this work, a new particle-based meshless method to simulate skeletal muscle tissue was introduced.