Ankle torque estimation based on disturbance observers for robotic rehabilitation

Designing safe and tailored strategies for robotic therapy requires the knowledge of patient joint torques, allowing control strategies to adjust the torque level provided by the robotic device according to the patient’s performance. Given the impracticability of measuring human joint torques directly, many works in the area have used estimation techniques that require complex calibration and signal processing or introduce uncertainty in their system modeling. This paper evaluates three disturbance observer techniques for estimating ankle joint torque as an alternative solution. Based on the generalized momentum and Kalman filter methodologies, the approaches were implemented on a robotic device for ankle rehabilitation. They were evaluated on six healthy voluntary users for sitting position movements. The techniques demonstrated effectiveness in estimating human joint torque across three distinct human–robot interaction modes, with performance evaluation through normalized root-mean-square error (NRMSE) metrics. Statistical analysis, including ANOVA, Kruskal–Wallis, and Dunn’s post hoc tests, was employed to assess approach performance and impact effects under different configuration settings. These analyses highlighted significant differences in performance among the techniques, enhancing the understanding of the estimation approaches and highlighting their potential integration into robotic rehabilitation settings.