A Data-driven Gain Adaptation Mechanism for Flexible Usability in the UGent Knee Rig. Issue 1 (July 2017) - Chevalier, Amélie; De Vlieger, Brecht; Verstraete, Matthias; Ionescu, Clara; De Keyser, Robin
Abstract: This paper presents a data-driven gain adaptation mechanism in order to make the usability of the UGent Knee Rig (UGKR) more flexible. The design of the UGKR is unique as it is characterized by a movable ankle joint contrary to traditional dynamic knee rigs which are used to investigate knee kinematics and surgical techniques. This feature allows the UGKR to perform both bicycle motions and squat motions while applying a quadriceps force and hamstring forces. The control of the UGKR is a model-based control strategy which requires time-consuming system identification for each new knee specimen. A data-driven gain adaptation mechanism is developed reducing the necessary time for identification creating a flexible usability. Two adaptation mechanisms are implemented and tested on both a mechanical hinge and a cadaver knee: a rectangular sequence and a polar sequence. The results from squat and bicycle tests indicate that rectangular sequence provides good performance for the rigid mechanical hinge, however, it fails for the elastic cadaver knee during full extension. It is shown that the polar sequence results in good performance for the cadaver knee compared to the rectangular mechanism.