A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot

Zeng, Xiangfeng; Zhu, Guoli; Lan, Yue; Zhang, Mingming; Xie, Shane

doi:10.1155/2017/6819056

Cited by 18 publications

(17 citation statements)

References 31 publications

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“…Other Researchers used a flickering action video to evoke steady state visual evoked potential (SSVEP). Combine SSVEP signals and VR, a computer can give motor intention during ankle robot training [33]. SSVEP based BCI paradigm could detect user's watching and give feedbacks and activate MNS at the same time while experiencing VR contents.…”

Section: Neurorehabilitation Using Virtual Realitymentioning

confidence: 99%

Novel Virtual Reality Application in Field of Neurorehabilitation

Kang

2018

Brain Neurorehabil

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Section: Neurorehabilitation Using Virtual Realitymentioning

confidence: 99%

Novel Virtual Reality Application in Field of Neurorehabilitation

Kang

2018

Brain Neurorehabil

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“…The ankle robot and its control strategies is the same as the one applied in [8], which are briefly described as below separately. The ankle robot have three ROM, through which subjects can realize ankle rehabilitation training along DF/PF, INV/EV, and adduction/abduction (AA).…”

Section: A Ankle Rehabilitation Robot and Control Strategiesmentioning

confidence: 99%

“…In other words, under the assist-as-needed strategy, subjects can accomplish the training successfully only if they can trigger the robot. Zeng et al, [8] proposed a SSVEP-based passive training on an ankle rehabilitation robot, advantage of which is enable subject without enough motion ability to conduct active training have opportunities to conduct ankle training based on his own motion ability, but the limitation of which is in that subjects only have one chance to display their motion intention in a single training. However when in active training, subjects need run several rounds of displaying motion intention to touch the targets.…”

Section: Introductionmentioning

confidence: 99%

“…Identification of SSVEP signals can be conducted through different algorithms, which include ICA, PCA, CCA and so on [9]. The basic idea of those algorithms come from below several points: 1 to remove noise signal from EEG signal to get SSVEP more easy to be distinguished; 2) correlation analysis to identify SSVEP signal through constructing a similar periodic signal, or extracting homologous signals from EEG signals from surrounding different electrodes; 3) to extract the principal component from EEG signals [10].…”

Section: Introductionmentioning

confidence: 99%

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A Feasibility Study of Robot-Assisted Ankle Training Triggered by Combination of SSVEP Recognition and Motion Characteristics

Zeng

Zhu

et al. 2018

2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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In order to inspire subjects exerting more energy and pay more attention to SSVEP-based ankle training, this study introduce motion intention detection both in the first half cycle of single trainings and at the beginning of the training. This study also propose a novel method to recognize motion intention of subjects through merging the motion characteristics of the ankle training into the identification of SSVEP signals. Five healthy subjects participate in the training, and all can accomplish the training with the success rate of more than 80%. The proposed hybrid method can increase success rate from 50% to 80% comparing with the identification of SSVEP signals.

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“…Continuous passive movement (CPM) machines and simple rehabilitation treadmills, which are widely used in clinical practice, generally only provide passive training modes. An ankle rehabilitation robot based on steady-state visual evoked potentials was discussed in Reference [19], which combined SSVEP signals with a virtual reality environment and was able to determine the subject's intention to perform passive training. In addition, for rule-based control structures, a human-machine interface was presented in Reference [20].…”

mentioning

confidence: 99%

Novel Design and Adaptive Fuzzy Control of a Lower-Limb Elderly Rehabilitation

Zhang

Chen

et al. 2020

Electronics

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Design and control of a lower-limb exoskeleton rehabilitation of the elderly are the main challenge for health care in the past decades. In order to satisfy the requirements of the elderly or disabled users, this paper presents a novel design and adaptive fuzzy control of lower-limb empowered rehabilitation, namely MOVING UP. Different from other rehabilitation devices, this article considers active rehabilitation training devices. Firstly, a novel product design method based on user experience is proposed for the lower-limb elderly exoskeleton rehabilitation. At the same time, in order to achieve a stable operation control for the assistant rehabilitation system, an adaptive fuzzy control scheme is discussed. Finally, the feasibility of the design and control method is validated with a detailed simulation study and the human-interaction test. With the booming demand in the global market for the assistive lower-limb exoskeleton, the methodology developed in this paper will bring more research and manufacturing interests. Electronics 2020, 9, 343 2 of 17 the effectiveness of rehabilitation training. A wearable upper limb therapy robot based on Pneumatic Muscle Actuator (PMA) was introduced in Reference [13]. This device has five degrees of freedom, works similarly to human muscle functions, has high compliance and safety. Besides, Reference [14] developed a cable drive for the exoskeleton, which has seven degrees of freedom and can be used in medical, remote control, test research, and other fields.In general, the structure of the exoskeleton rehabilitation device is simple, but it is challenging to carry out rehabilitation training for the local body parts, and the rehabilitation effect is not ideal [15,16]. The research of the end-effector rehabilitation device is mainly used in limb rehabilitation, remote control, and other fields, and its research direction focuses on improving freedom, ensuring safety and reducing complexity, and so forth. Existing exoskeleton rehabilitation devices are complicated and expensive, and they are mostly used in experiments and difficult to achieve marketization. Both types of rehabilitation devices have their own advantages and disadvantages. It is necessary to select a suitable rehabilitation device type in specific applications and match different training modes to achieve a better training effect [17].The early version in Reference [18] developed by the Federal Institute of Technology in Zurich, was passive gait training. Continuous passive movement (CPM) machines and simple rehabilitation treadmills, which are widely used in clinical practice, generally only provide passive training modes. An ankle rehabilitation robot based on steady-state visual evoked potentials was discussed in Reference [19], which combined SSVEP signals with a virtual reality environment and was able to determine the subject's intention to perform passive training. In addition, for rule-based control structures, a human-machine interface was presented in Reference [20]. The robot manipulator (RM) can pe...

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A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot

Cited by 18 publications

References 31 publications

Novel Virtual Reality Application in Field of Neurorehabilitation

Novel Virtual Reality Application in Field of Neurorehabilitation

A Feasibility Study of Robot-Assisted Ankle Training Triggered by Combination of SSVEP Recognition and Motion Characteristics

Novel Design and Adaptive Fuzzy Control of a Lower-Limb Elderly Rehabilitation

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