# Treadmill Deck Performance Optimization Design Based on Muscle Activity during Running
## 1 Introduction  
Running has gained popularity as one of the most common and important forms of physical exercise. Currently, popular running methods can be divided into two types, running on the ground and running on a treadmill. Ground running is one of the simplest and earliest forms of running, requiring little extra equipment. But ground running can be affected by many environmental factors, such as road conditions and weather. Therefore, more and more people are choosing to run on treadmills that can allow them to run indoors. Treadmills are frequently utilized in a variety of settings, such as physical therapy, exercise testing, rehabilitation, and research. However, as one of the most common running surfaces, the existing standard only defines the structural safety aspects of the treadmill while neglecting to specify and study the mechanical properties of the surface [1, 2]. Similarly, there is no clarity on the mechanical features of treadmills in the numerous scientific studies comparing treadmills to running on the ground [3, 4, 5]. In fact, the mechanical properties of the treadmill deck have an important effect on physiological response. It has been reported that athletes adjust leg stiffness and dynamics when running on surfaces with different mechanical properties [6, 7]. During running, the body is continuously subjected to repetitive impact forces that are caused by GRF during the stance phase. Repeated impactforce during exercise is the main cause of chronic injury and overuse injury [8; 9]. Therefore, the characteristics of the sports surface itself are one of the important factors in the running process, because they are related to the performance and safety of the runner. The potential effects of GRF and injuries during running have been widely discussed. A number of multi-degree-of-freedom models have been developed to study the biomechanics of human gait, given the intricate nature of the human body, which consists of multiple interconnected systems. Among them, the MSD model is the most commonly used method to study human gait characteristics. [PERSON] [10] and [PERSON] [11] have proposed some major MSD models that assess the GRF during the stance phase of human running. [PERSON] et al. [12] developed a multi-body model of the anthropomorphic lower limb that also takes into account the movement of soft tissue blocks relative to the bone to obtain more accurate estimates of bone kinematics and the distribution of internal forces in the leg. [PERSON] and [PERSON] [13] proposed a 4-degree-of-freedom MSD model called the LN model. This model is the first to comprehensively consider the rigid mass and wobbling mass of the upper and lower limbs of the human body. In addition, the shoe-foot nonlinear model is used to study the GRF in the stance phase of human running, so the LN model has received wide attention. In fact, GRF is a result of model calculations and is consistent with experimental results. Subsequently, [PERSON] et al. [14] modified some of the parameters of the LN model to minimize the difference between the numerical and experimentally obtained GRF peaks. While some researchers believed that stiffness and damping properties of shoes and sports surfaces alter GRF characteristics [15; 16], nevertheless, research with expert runners has demonstrated that changing the hardness parameters of footwear and active surfaces has little impact on GRF [17]. The GRF's dominant frequency is relatively close to the natural frequency of LLST [18], so during running the calf muscle may experience a resonance phenomenon, increasing soft tissue vibration amplitude. However, running does not exhibit such phenomena according to experimental studies [19]. Experiments have shown that the stiffness of leg muscles varies with the environment, such as the hardness of shoes and sports surfaces. As a result of these physiological phenomena, some researchers have proposed a theory of muscle activity based on central nervous system (CNS) control [20; 21; 22; 23].