Kinesthetic teaching of a therapist's behavior to a rehabilitation robot

Fong, Jason; Tavakoli, Mahdi

doi:10.1109/ismr.2018.8333285

Cited by 31 publications

(12 citation statements)

References 16 publications

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“…The identification of the remaining ability of patients is more complex [5,7,10], without further identifying whether the patients are in a state of needing assistance when completing the task, which has great limitations. Nonetheless, because of the advantages of the GMM algorithm in data modeling [15], and how GMM has been successfully applied in robot demonstration learning [16][17][18], this paper proposes an AAN control strategy based on GMM in order to solve the problem of whether patients need assistance when completing tasks. Firstly, a new end-effector bilateral mirror upper limb rehabilitation robot was designed for patients with upper limb motor dysfunction, differentiating it from other upper limb rehabilitation robots [18,21] that can only be provided by the virtual environment, which cannot accurately express the real intention of the patient.…”

Section: Discussionmentioning

confidence: 99%

“…The model can parameterize a set of data points and its underlying functions into a weighted sum of the Gaussian component density, each of which has its own mean and covariance. Due to the simplicity of Gaussian functions, strong adaptability and the advantages of generative modeling, GMM has been widely used in robot demonstration learning (LFD) [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Assist-As-Needed Control Strategy of Bilateral Upper Limb Rehabilitation Robot Based on GMM

Zhang

Zuo

et al. 2022

Machines

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Robotic-assisted rehabilitation therapy has been shown to be effective in improving upper limb motor function and the daily behavior of patients with motor dysfunction. At present, the majority of upper limb rehabilitation robots can only move in the two-dimensional plane, and cannot adjust the assistance mode in real-time according to the patient’s rehabilitation needs. In this paper, according to the shortcomings of the current rehabilitation robot only moving in the two-dimensional plane, a type of bilateral mirror upper limb rehabilitation robot structure with the healthy side assisting the affected side is proposed. This can move in three-dimensional space. Additionally, an assist-as-needed (AAN) control strategy for upper limb rehabilitation training is proposed based on the bilateral upper limb rehabilitation robot. The control strategy adopts Gaussian Mixture Model (GMM) and impedance controller to maximize the patient’s rehabilitation effect. In the task’s design, there is no need to rely on the assistance of the therapist, only the patients who completed the task independently. GMM guides the rehabilitation robot to provide different assistance for the patients at different task stages and induces the patients to complete the rehabilitation training independently by judging the extent to which the patients can complete the task. Furthermore, in this paper, the effectiveness of the proposed control strategy was verified by three volunteers participating in a two-dimensional task. The experimental results show that the proposed AAN control strategy can effectively provide appropriate assistance according to the classification stage of the interaction between the patients and the rehabilitation robot, and thus, patients can better achieve the rehabilitation effect during the rehabilitation task as much as possible.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assist-As-Needed Control Strategy of Bilateral Upper Limb Rehabilitation Robot Based on GMM

Zhang

Zuo

et al. 2022

Machines

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“…An admittance-based controller will be used for the robot and automatically control the probe's scanning force applied to the tissue. The admittance controller produces a desired position using a predefined relationship between the position and measured force (Zeng and Hemami, 1997 ; Fong and Tavakoli, 2018 ). The US scanning assistant is shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Robotic Ultrasound Scanning With Real-Time Image-Based Force Adjustment: Quick Response for Enabling Physical Distancing During the COVID-19 Pandemic

et al. 2021

Self Cite

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During an ultrasound (US) scan, the sonographer is in close contact with the patient, which puts them at risk of COVID-19 transmission. In this paper, we propose a robot-assisted system that automatically scans tissue, increasing sonographer/patient distance and decreasing contact duration between them. This method is developed as a quick response to the COVID-19 pandemic. It considers the preferences of the sonographers in terms of how US scanning is done and can be trained quickly for different applications. Our proposed system automatically scans the tissue using a dexterous robot arm that holds US probe. The system assesses the quality of the acquired US images in real-time. This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame. The quality assessment algorithm is based on three US image features: correlation, compression and noise characteristics. These US image features are input to the SVM classifier, and the robot arm will adjust the US scanning force based on the SVM output. The proposed system enables the sonographer to maintain a distance from the patient because the sonographer does not have to be holding the probe and pressing against the patient's body for any prolonged time. The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue. The result of the experiments shows us that our proposed quality assessment algorithm successfully maintains US image quality and is fast enough for use in a robotic control loop.

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“…LfD has proven to be an effective way of teaching robots important motion skills that are necessary when assisting people and providing health care services [45]. Specifically, LfD approaches have been used to teach robots a variety of skills, e.g., physical rehabilitation [46], hand rehabilitation [47], motion planning for rehabilitation [48], robotic surgery [49], and feeding [50], among others.…”

Section: Introductionmentioning

confidence: 99%

Passive Exercise Adaptation for Ankle Rehabilitation Based on Learning Control Framework

Abu‐Dakka

Valera

Escalera

et al. 2020

Sensors

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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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Kinesthetic teaching of a therapist's behavior to a rehabilitation robot

Cited by 31 publications

References 16 publications

Assist-As-Needed Control Strategy of Bilateral Upper Limb Rehabilitation Robot Based on GMM

Assist-As-Needed Control Strategy of Bilateral Upper Limb Rehabilitation Robot Based on GMM

Robotic Ultrasound Scanning With Real-Time Image-Based Force Adjustment: Quick Response for Enabling Physical Distancing During the COVID-19 Pandemic

Passive Exercise Adaptation for Ankle Rehabilitation Based on Learning Control Framework

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