Development of Personalized Human Digital Twin for Exercising

Exercise requires quantitative planning and monitoring. Digital approaches offer a potential solution; however, developing subject-specific digital twin (DT) models remains challenging. Here, we propose a method to establish personalized human digital twin for exercising. Compared to the conventional methods that relies on medical imaging, we identify personalized biomechanical parameters, i.e., maximum isometric muscle force, based on the electromyographic (EMG) signals, kinematics data, and interaction forces. It allows for rapid and cost effective creation of individualized musculoskeletal models. Application studies demonstrate that our approach can accurately model the back squat movement in five subjects, yielding normalized RMSE of 0.2 for the ankle and hip joints, and 0.3 for the knee joints. These advantages, as a result, indicate a convincing strong potential for personalized exercise planning and monitoring.