Method for estimating physical interaction forces using human–exoskeleton kinematic modelling and energy optimization

Exoskeleton robots mimic the structure of the human body and are physically connected to the wearer through attachment parts such as cuffs or straps. Nevertheless, misalignment between the human body and the robot can occur due to improper wearing of the exoskeleton, the elasticity of human skin, and the geometric complexity of human joint movements. Such misalignment increases unnecessary physical interaction forces, causing discomfort and pain to the wearer. Therefore, these interaction forces should be considered when designing exoskeleton robots to ensure wearability. In this study, we propose a method for estimating human-exoskeleton interaction forces through posture prediction. The human-robot connection is modeled as an elastic element, and posture is predicted using an energy optimization algorithm. The predicted posture is then used to calculate the interaction forces. Since this method considers only the physical characteristics of the exoskeleton robot and wearer, it enables objective evaluation of the robot without the need to manufacture actual prototypes. We performed quantitative experiments using prototypes of a sensor-equipped dummy and an exoskeleton to confirm the effectiveness of the modeling method. Consequently, the proposed method is expected to reduce the time and costs associated with developing exoskeleton robots and obviate the need for human subject testing.