Inverse Kinematics to Simulate Sport Movements in Virtual Environment

Performance diagnostics, such as monitoring athletes’ movements, are essential for enhancing their performances. Wearable sensor networks are commonly employed for this purpose. However, designing such networks can be complex and often necessitates expensive and time-consuming pilot studies. To address this, we propose utilizing multibody models and digital sensors to simulate human movements and virtually test the development of sensor setups. In this study, we present an improved multibody model of the human upper limb, building upon an existing virtual environment from the literature for simulating human gestures. Our model incorporates a streamlined representation of the human torso and clavicle, along with their corresponding joints. The primary objective is to introduce a novel actuation method for the updated model, which only requires the positions of a predefined point as input, utilizing inverse kinematics to define the joint parameters. This approach reduces the amount of data required for simulation. To showcase the capabilities of this method, we acquired and utilized the trajectories of the center of gravity of the hand of a handball player executing a set shot to animate the multibody model. During the user study, the reference trajectories were compared to the trajectories obtained for the hand model during the simulation, revealing a strong agreement and affirming the feasibility of the proposed method.