Human Arm Impedance Estimation in a Ball-Bouncing Task Using EMG-Driven Model and Unscented Kalman Filter

Human-robot interaction (HRI) requires robotic systems to adapt to human movement. Human arm impedance, a key factor in this tuning process, is typically estimated using either perturbation methods or electromyography (EMG)-driven models. The latter allows continuous estimation by calculating muscle forces based on sensor signals, but only computes stiffness. However, damping estimation is equally crucial for stability, and due to its nonlinear nature, accurate estimation remains difficult. In this study, we employ an EMG-driven model to estimate arm stiffness during a ballbouncing task, followed by computing the damping profile using an Unscented Kalman Filter. The estimated impedance profiles are then validated against existing literature and used to replicate a ball-bouncing movement recorded during experiments. A task chosen as it presents cyclic upper-limb motion with intermittent environmental interaction.