Zeng Wang

Research shows that treadmill shock-absorbing devices can reduce the impact of ground reaction forces on the knee and ankle joints during running. Most existing treadmills use fixed or passive shock absorption, meaning their shock-absorbing systems do not actively adjust to changes in ground reaction forces (GRFs). Methods: This study establishes a mathematical model integrating human motion biomechanics and treadmill running surfaces, analyzing the relationships between various parameters affecting the system. Ultimately, an optimal shock-absorbing treadmill control system is designed, utilizing a microcontroller as the main control unit, airbags for shock absorption, and a widely used foot pressure testing system. Objective: The goal is to more effectively prevent running injuries caused by excessive foot pressure. Compared to conventional shock absorption systems, this design features an active multilevel adjustment function with higher precision in regulation. Results: The experimental results demonstrate that the ground reaction force (GRF) generated by the optimal shock-absorbing treadmill control system is reduced by up to 10% compared to that of a conventional shock-absorbing treadmill. Conclusions: This leads to a smaller impact force on the knees due to foot pressure, resulting in better injury prevention outcomes.

In previous research on treadmills, the main focus has been on comparing the physiological differences induced by running on treadmill decks and other exercise surfaces, with relatively little research on the mechanical properties of treadmill decks. Reducing sports injuries is a common desire of runners, which may be closely related to muscle activity. Obviously, the mechanical properties of the treadmill play an important role in this process. Muscle activity was evaluated based on a mass-spring-damper (MSD) model that provides a simulated signal of the ground reaction forces (GRF) and vibration of the lower-limb soft tissues (LLST) during the landing of the human body during running. We improved the original human motion model by considering the stiffness and damping effect of the treadmill deck. In addition, based on the theory of muscle activity regulation, the dimensionless objective function is established, and the particle swarm optimization algorithm is used to find the best range of treadmill deck parameters under pre- and post-fatigue conditions. The results show that the hardness of the treadmill deck can affect the regulation of muscle activity. Based on this, the parameters of the specific safe area of the treadmill deck are obtained, and the size of the safe area after fatigue is significantly reduced compared to that before fatigue. By studying the physiological effects of the mechanical properties of the treadmill deck on runners, the research results are expected to provide references for the design of treadmill deck parameters and reduce the risk of runners’ sports injuries, which has practical application value for treadmill design and runners’ health.

The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and the failure to detect quality issues promptly in accordance with manufacturing settings, which may trigger the propagation of downstream defects. Currently, most inspection quality methods consist of direct measurements followed by manual judgment. The integration of deep learning methods provides a feasible way in which to identify defects on time, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model, built around discrete manufacturing characteristics, and uses fuzzy theory to evaluate the quality levels of production stages. Furthermore, a multivariate long- and short-term memory sequence model is proposed in order to explore the qualitative information from time domain features. The data regarding the produced water dispensers are validated using three evaluation indices, namely, RMSE, MAE, and MAPE. The results indicate that the multivariate long- and short-term memory model exhibits stronger prediction performance.