A Comparison Between Electromyography-Driven Robot and Passive Motion Device on Wrist Rehabilitation for Chronic Stroke

Hu, Xiao; Tong, Kai-Yu; Song, Rong; Zheng, Xiujuan; Leung, Wallace Woon‐Fong

doi:10.1177/1545968309338191

Cited by 178 publications

(160 citation statements)

References 23 publications

(53 reference statements)

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“…Furthermore, learning in TIFT was disjointed early in the training, with subjects often hitting the path walls, stopping and trying several strategies before determining the correct shoulder-elbow coordination needed to advance within the path. Nevertheless, for stroke rehabilitation, we believe further study into assistance modes such as TIFT is needed, given the recent negative findings in robots that have used the TD training mode [42][43] and the evidence that passive movement training does not improve motor control [82][83]. For example, Bluteau et al found that in a drawing task, haptic guidance with a position-based controller similar to our TD training mode was inferior to a force-based controller for motor learning [84].…”

Section: Discussionmentioning

confidence: 99%

“…Electrophysiology experiments have demonstrated that afferent feedback can facilitate plasticity in the motor system if it arrives synchronously with ongoing motor output [59][60]. This has motivated a number of robotic strategies to assure proper timing between effort and movement, including movement of the contralateral limb to control movement of the paretic limb in bilateral tasks [32,[85][86] and EMG-controlled movement [83,[87][88]. The TIFT mode assures proper timing between effort and movement by presenting a passive environment to the limb, such that no energy is injected into the limb.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Retraining of interjoint arm coordination after stroke using robot-assisted time-independent functional training

Brokaw¹,

Murray²,

Nef³

et al. 2011

JRRD

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Abstract-We have developed a haptic-based approach for retraining of interjoint coordination following stroke called time-independent functional training (TIFT) and implemented this mode in the ARMin III robotic exoskeleton. The ARMin III robot was developed by Drs. Robert Riener and Tobias Nef at the Swiss Federal Institute of Technology Zurich (Eidgenossische Technische Hochschule Zurich, or ETH Zurich), in Zurich, Switzerland. In the TIFT mode, the robot maintains arm movements within the proper kinematic trajectory via haptic walls at each joint. These arm movements focus training of interjoint coordination with highly intuitive real-time feedback of performance; arm movements advance within the trajectory only if their movement coordination is correct. In initial testing, 37 nondisabled subjects received a single session of learning of a complex pattern. Subjects were randomized to TIFT or visual demonstration or moved along with the robot as it moved though the pattern (time-dependent [TD] training). We examined visual demonstration to separate the effects of action observation on motor learning from the effects of the two haptic guidance methods. During these training trials, TIFT subjects reduced error and interaction forces between the robot and arm, while TD subject performance did not change. All groups showed significant learning of the trajectory during unassisted recall trials, but we observed no difference in learning between groups, possibly because this learning task is dominated by vision. Further testing in stroke populations is warranted.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Retraining of interjoint arm coordination after stroke using robot-assisted time-independent functional training

Brokaw¹,

Murray²,

Nef³

et al. 2011

JRRD

View full text Add to dashboard Cite

show abstract

“…The hand contains 16 joints, which are differentially driven by a set of five independent actuators. Hu et al (2009) have presented a comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke patients. By comparative study, it was found that the EMG-driven interactive training had a better long-term effect than the continuous passive movement (CPM) treatment.…”

Section: Prior Research Related To Emg Applicationsmentioning

confidence: 99%

Design and Control of an EMG Driven IPMC Based Artificial Muscle Finger

Jain¹,

Datta²,

Majumder³

2012

Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges

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“…Application of robot-assisted arm training in patients with Parkinson's disease was recently reported in [9]. A comparison between EMG-driven and passive devices used on wrist rehabilitation with chronic stroke patients is presented in [10], proving interactive training to be superior to passive training.…”

Section: Introductionmentioning

confidence: 99%

Active Elbow Orthosis

Ripel

Krejsa

Hrbáček

et al. 2014

International Journal of Advanced Robotic Systems

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This paper presents a novel approach to the design of a motorized rehabilitation device -active elbow orthosis (AEO) -inspired by the principles of robotic exoskeletons. The device is currently designed for the elbow joint, but can be easily modified for other joints as well. AEO determines the motion activity of the patient using a strain gauge and utilizes this measurement to control the actuator that drives the forearm part of the orthosis. Patient activity level is related to a free arm measurement obtained via a calibration procedure prior to the exercise. A high-level control module offers several types of exercises mimicking the physiotherapist. The device was successfully verified by tests on a number of patients, resulting in extended range of elbow-joint motion.

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A Comparison Between Electromyography-Driven Robot and Passive Motion Device on Wrist Rehabilitation for Chronic Stroke

Abstract: . A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke. Neurorehabilitation and Neural Repair, 23(8) This is the Pre-Published Version.

Cited by 178 publications

References 23 publications

Retraining of interjoint arm coordination after stroke using robot-assisted time-independent functional training

Retraining of interjoint arm coordination after stroke using robot-assisted time-independent functional training

Design and Control of an EMG Driven IPMC Based Artificial Muscle Finger

Active Elbow Orthosis

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