Abstract
In order to better perform rehabilitation training on the ankle joint complex in the direction of dorsiflexion/plantarflexion and inversion/eversion, especially when performing the isokinetic muscle strength exercise, we need to calibrate the kinematic model to improve its control precision. The ankle rehabilitation robot we develop is a parallel mechanism, with its movements in the two directions driven by two linear motors. Inverse solution of positions is deduced and the output lengths of the two UPS kinematic branches are calibrated in the directions of dorsiflexion, plantarflexion, inversion, and eversion, respectively. Motion of each branch in different directions is fitted in high-order form according to experimental data. Variances, standard deviation, and goodness of fit are taken into consideration when choosing the best fitting curve, which ensures that each calibration can match the most appropriate fitting curve. Experiments are conducted to verify the effectiveness of the kinematic calibration after finishing the calibration, and the errors before and after calibration of the two kinematic chains in different directions are compared, respectively, which shows that the accuracy after calibration has been significantly improved.
Highlights
IntroductionRehabilitation training is an effective way to help patients restore their ankle joint complex’s (AJC) motor abilities for patients with ankle injuries
We focus on the kinematic calibration of our 2-UPS/RRR parallel ankle rehabilitation robot (PARR) according to field experiments and highorder data fitting, with variances, standard deviation, and goodness of fit as the criterion of fitting evaluation. e rest of the paper is organized as follows
By comparison with the theoretical inputs of l1 and l2 deduced from the inverse solution of positions without calibration, we can verify the effectiveness of the kinematic calibration of the parallel rehabilitation mechanism. e prototype of the 2-UPS/RRR PARR system used is as in Figure 13, with its human machine interface of the upper computer developed using C# programming language
Summary
Rehabilitation training is an effective way to help patients restore their ankle joint complex’s (AJC) motor abilities for patients with ankle injuries. To augment conventional physical therapy, many robotic ankle rehabilitation devices have been developed to provide repetitive, task-specific, interactive treatment of the impaired limb and monitor its motor recovery [2,3,4,5,6]. Passive rehabilitation training, which means that the AJC is driven by the robot for rehabilitation at a predetermined trajectory, is often used in the early stage of ankle therapy. Many ankle rehabilitation robots have realized the trajectory tracking control for passive rehabilitation training [7,8,9]. Among different kinds of ankle rehabilitation robots, the platform ones are better suited for ankle exercises [12], and parallel mechanism is the most common among them with the above-mentioned exercise modes [5, 6, 13, 14]
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