## 1 Introduction  
Stroke is characterized by a high incidence and disability rate [1; 2; 3], which can place a heavy burden on patients and their families. The signs and symptoms after stroke onset are usually unilateral lower limb motor deficits [4]. The current approach to stroke rehabilitation relies on the joint participation of rehabilitation physicians and physical therapists, a rather lengthy and expensive process, and the most critical problem is the huge shortage of rehabilitation professionals [5]. However, rehabilitation robots can effectively and economically provide rehabilitation to physically disabled patients compared to the traditional manual assistance of rehabilitation physicians. The rehabilitation effects of lower limb rehabilitation robots have been widely recognized [6; 7]. The biomechanics of human-exoskeleton interactions influence the redistribution of load and unload in body regions and are critical to the safety and effectiveness of the device [8]. However, if exoskeletal robots are not designed properly, long-term use can cause harm to the human body [9; 10]. To validate the effectiveness of exoskeletons, the traditional engineering approach is to create a simple physical model for testing and improve the design by iteratively building a new prototype, but the cycle time of this approach is too long. There would be a significant potential risk if prototypes were tested directly on patients. In addition to long-term and short-term experimental studies, musculoskeletal models can be used for simulation-assisted exoskeleton development, optimization, and evaluation, and can be an important tool for quantifying work-related load and exoskeletal impact. Musculoskeletal models are only able to calculate muscle forces and joint reaction forces from the input of a given motion. The field of musculoskeletal modeling has evolved a lot in the last two decades. There are many musculoskeletal modeling and simulation software available. Among the most popular ones are OpenSim and AnyBody Modeling System [11; 12]. In the simulation system, the human body wears an exoskeleton to perform various movements and collects data on the interaction between the exoskeleton and the human to evaluate the performance of the exoskeleton in a more cost-effective and time-efficient manner. [PERSON] used OpenSim to compare the changes in joint moments with and without the exoskeleton [13]. However, the simulations did not consider the properties of the muscles. Similarly, [PERSON] et al. used OpenSim and ADAMS to model the exoskeleton. The objective was to measure the alignment of the joints between the human body and the exoskeleton. A fitted kinetic equation was also developed to calculate the joint moments based on the virtual prototype [14]. Compared to OpenSim software, AnyBody has an open-source code model library. With the AnyBody model library, users can use and modify new models more easily, which greatly simplifies modeling. [PERSON] used AnyBody software to simulate the process of moving an object with the human body [15]. A comparative analysis of the reaction law of the object on the human body was performed by varying the weight of the object. Studies have used simulation software to output the joint torques of exoskeletons and evaluate joint activity control performance [16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. [PERSON] et al. designed a virtual prototype set-up of a rehabilitation exoskeleton by merging computational musculoskeletal analysis and AnyBody simulation technology [16]. [PERSON] used AnyBody simulation to study and analyze the performance of the lower limb exoskeleton. The experimental results showed that enhancement through the exoskeleton can lead to a significant reduction in muscle loading [17]. In order to make the structure of the lower limb exoskeleton more compact and simpler and provide a theoretical basis for the dynamic support effect of the lower limb exoskeleton, [PERSON] used AnyBody to simulate the knee joint lower limb exoskeleton.