Development of lower-limb rehabilitation exercises using 3-PRS Parallel Robot and Dynamic Movement Primitives

The design of rehabilitation exercises applied to sprained ankles requires extreme caution, regarding the trajectories and the speed of the movements that will affect the patient. This paper presents a technique that allows a 3-PRS parallel robot to control such exercises, consisting of dorsi/plantar flexion and inversion/eversion ankle movements. The work includes a position control scheme for the parallel robot in order to follow a reference trajectory for each limb with the possibility of stopping the exercise in mid-execution without control loss. This stop may be motivated by the forces that the robot applies to the patient, acting like an alarm mechanism. The procedure introduced here is based on Dynamic Movement Primitives (DMPs).

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Passive Exercise Adaptation for Ankle Rehabilitation Based on Learning Control Framework

Sensors ◽

10.3390/s20216215 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6215

Author(s):

Fares J. Abu-Dakka ◽

Angel Valera ◽

Juan A. Escalera ◽

Mohamed Abderrahim ◽

Alvaro Page ◽

...

Keyword(s):

Plantar Flexion ◽

Learning Control ◽

Parallel Robot ◽

Ankle Injuries ◽

Simulink Model ◽

Exercise Adaptation ◽

Learning Techniques ◽

Ankle Rehabilitation ◽

Dynamic Movement Primitives ◽

Rehabilitation Exercises

Ankle injuries are among the most common injuries in sport and daily life. However, for their recovery, it is important for patients to perform rehabilitation exercises. These exercises are usually done with a therapist’s guidance to help strengthen the patient’s ankle joint and restore its range of motion. However, in order to share the load with therapists so that they can offer assistance to more patients, and to provide an efficient and safe way for patients to perform ankle rehabilitation exercises, we propose a framework that integrates learning techniques with a 3-PRS parallel robot, acting together as an ankle rehabilitation device. In this paper, we propose to use passive rehabilitation exercises for dorsiflexion/plantar flexion and inversion/eversion ankle movements. The therapist is needed in the first stage to design the exercise with the patient by teaching the robot intuitively through learning from demonstration. We then propose a learning control scheme based on dynamic movement primitives and iterative learning control, which takes the designed exercise trajectory as a demonstration (an input) together with the recorded forces in order to reproduce the exercise with the patient for a number of repetitions defined by the therapist. During the execution, our approach monitors the sensed forces and adapts the trajectory by adding the necessary offsets to the original trajectory to reduce its range without modifying the original trajectory and subsequently reducing the measured forces. After a predefined number of repetitions, the algorithm restores the range gradually, until the patient is able to perform the originally designed exercise. We validate the proposed framework with both real experiments and simulation using a Simulink model of the rehabilitation parallel robot that has been developed in our lab.

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