A Novel Telerehabilitation System Based on Bilateral Upper Limb Exoskeleton Robot

Ji, Chen; Hu, Dingyin; Sun, Wentao; Tu, Xikai; He, Jiping

doi:10.1109/rcar47638.2019.9044072

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…In recent decades, many research groups have been developing sophisticated robotic devices that can be operated remotely and be worn by a person to manage the remote slave robots [27], [28]. Zhang et al [29] developed a telerehabilitation system based on an exoskeleton device that can be controlled remotely as a slave device by a therapist through a master device, but the therapist can only monitor the patient's status visually via a web camera.…”

Section: Related Workmentioning

confidence: 99%

Interactive IIoT-Based 5DOF Robotic Arm for Upper Limb Telerehabilitation

et al. 2022

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Significant advancements in contemporary telemedicine applications enforce the demand for effective and intuitive telerehabilitation tools. Telerehabilitation can minimize the distance, travel burden, and costs between rehabilitative patients and therapists. This research introduces an interactive novel telerehabilitation system that integrates the Industrial Internet of Things (IIoT) platform with a robotic manipulator named xARm-5, aiming to deliver rehabilitation therapies to individuals with upper limb dysfunctions. With the proposed system, a therapist can provide upper limb rehab exercises remotely using an augmented reality (AR) user interface (UI) developed using VuForia Studio, which transmits bidirectional data through the IIoT platform. The proposed system has a stable communication architecture and low teleoperation latency. Experimental results revealed that with the developed telerehabilitation framework, the xArm-5 could be teleoperated from the developed AR platform and/or use a joystick to provide standard upper limb rehab exercises. Besides, with the designed AR-based UI, a therapist can monitor rehab/robot trajectories along with the AR digital twin of the robot, ensuring that the robot is providing passive therapy for shoulder and elbow movements.

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Section: Related Workmentioning

confidence: 99%

Interactive IIoT-Based 5DOF Robotic Arm for Upper Limb Telerehabilitation

et al. 2022

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“…They used surface electromyography (sEMG) to classify the motion of the unaffected hand and had the hand exoskeleton designed to perform the same movement on the affected hand. Upper limb rehabilitation research was also proposed in [ 51 ] where researchers trained a model to predict wrist motion using EMG sensors (MyoWare sensors) to move an exoskeleton robot.…”

Section: Categorizationmentioning

confidence: 99%

“…Researchers in [ 42 , 43 , 49 ] used CSP for feature extraction and applied an SVM to classify EEG data for two-classes motor imagery classification and hand and wrist movement classification. Others adopted an SVM to handle EMG signals for gesture, gait phase and wrist position recognition [ 48 , 50 , 51 ]. Wang et al [ 52 ] applied an SVM to both the EMG and EEG signals for predicting the users’ intention and fatigue.…”

Section: Technical Attributesmentioning

confidence: 99%

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Digital Twin Coaching for Physical Activities: A Survey

Díaz

Ding

et al. 2020

Sensors

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Digital Twin technology has been rising in popularity thanks to the popularity of machine learning in the last decade. As the life expectancy of people around the world is increasing, so is the focus on physical activity to remain healthy especially in the current times where people are staying sedentary while in quarantine. This article aims to provide a survey on the field of Digital Twin technology focusing on machine learning and coaching techniques as they have not been explored yet. We also define what Digital Twin Coaching is and categorize the work done so far in terms of the objective of the physical activity. We also list common Digital Twin Coaching characteristics found in the articles reviewed in terms of concepts such as interactivity, privacy and security and also detail future perspectives in multimodal interaction and standardization, to name a few, that can guide researchers if they choose to work in this field. Finally, we provide a diagram for the Digital Twin Ecosystem showing the interaction between relevant entities and the information flow as well as an idealization of an ideal Digital Twin Ecosystem for team sports’ athlete tracking.

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