Predicting stress between human body and wearable equipment using the soft tissue mass-spring model

Abstract

One crucial aspect of wearable equipment is user comfort. Given that users directly wear these devices during their daily activities, the device's value may significantly diminish if the comfortability is not satisfactory. Moreover, these devices closely contact the human body, possibly causing skin injuries in severe cases. This raises the necessity of assessing the comfortability of wearable equipment. However, manufacturing actual prototypes and conducting human trials present numerous challenges. In such circumstances, the utility of dynamic simulation technology can be highly beneficial. The aim of this study is to predict the stress imposed on the human body by wearable equipment in a dynamic environment. Traditional finite element models face challenges in dynamic environments. To address this, a mesh model composed of a mass-spring system was developed using a dynamic solver. The accuracy of this mesh model was verified by comparing it with finite element models, and it was used to build a human soft tissue model. The model is designed to achieve higher accuracy than the 20% just noticeable difference (JND) that an average person can recognize in force. A 3D model of the wearable equipment, the GEMS exoskeleton robot, was constructed, and an interactive environment between the human body and the device was set up. The gait of a skeleton model wearing the exoskeleton robot, learned through reinforcement learning, was simulated, and the stress between the human body and the exoskeleton robot was predicted using the human soft tissue model. Additionally, the changes in skin stress when a cushion is added between the exoskeleton robot and the human body were investigated. Through this, the effectiveness of adding a cushion in reducing the stress imposed on the skin and improving comfortability was confirmed. This study validated the use of the mass-spring model of soft tissue in dynamic simulation to predict stress between wearable equipment and the human body. This suggests that it can serve as a useful tool in ensuring user experience during the design and testing process of wearable equipment.