Passive Exercise Adaptation for Ankle Rehabilitation Based on Learning Control Framework

Abstract

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.

Dynamic movement primitives in robotics: A tutorial survey

“…Gait-related assistance and rehabilitation with exoskeletons is a very common application of DMPs, and there are numerous examples (Abu-Dakka et al, 2015; Amatya et al, 2020; Escarabajal et al, 2023; Huang et al, 2016a; Hwang et al, 2019, 2021; Schaal, 2006; Yuan et al, 2020; Zou et al, 2021). In Abu-Dakka et al, 2015, 2020), a parallel robot was used for ankle rehabilitation, where the movements were generated by DMPs (Figure 20). A similar parallel robot was applied in Escarabajal et al (2023) for knee rehabilitation, where RevDMPs was used to enable a patient to reverse the movement in order to maintain their own desired pace.…”

“…The method in Xu et al (2023) used DMPs for leg exoskeleton movements in a mirror therapy concept where the motion of the healthy limb is transferred to the impaired limb. Hong et al (2023) used DMPs to plan obstacle-avoidance leg movements during walking with a prosthesis.

Figure 20.An example of using DMPs for teaching passive exercises for ankle rehabilitation (Abu-Dakka et al, 2015, 2020).

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“…An example of using DMPs for teaching passive exercises for ankle rehabilitation (Abu-Dakka et al, 2015, 2020). …”

Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

“…In addition, because the lower extremity exoskeleton rehabilitation robot interacts with the patient and the patient sends out an active movement intention as an input signal for the exoskeleton control, the human-computer interaction control algorithm is indispensable. In order to prevent the affected limb from confronting the exoskeleton due to abnormal muscle activities (such as spasm), interactive control should be able to provide a safe and active and flexible training method [15]. At the same time, in order to encourage patients to actively participate in rehabilitation training, so that patients have a sense of success, interactive control will obtain the patient's active movement intention from sensor signals to achieve active training.…”

Type Synthesis of Self-Alignment Parallel Ankle Rehabilitation Robot with Suitable Passive Degrees of Freedom

“…Liu et al [26] designed a compliant anklerehabilitation robot redundantly driven by pneumatic muscles and cables to provide full range of motion and torque ability for the human ankle. Abu-Dakka et al [27] developed a 3-PRS parallel ARR with three DOF that realized dorsiflexion (DO)/plantarflexion (PL), inversion (IN)/eversion (EV), and translation in the height direction. Zhang et al [28] and Liu et al [29] proposed compact generalized spherical parallel mechanisms suitable for ankle rehabilitation based on an ankle-motion fitting model with a high matching degree.…”