Hybrid Brain/Muscle Signals Powered Wearable Walking Exoskeleton Enhancing Motor Ability in Climbing Stairs Activity

Li, Zhijun; Yuan, Ye; Luo, Ling; Su, Wenbin; Zhao, Kongshuang; Xu, Cuichao; Huang, Junliang; Pi, Ming Hong

doi:10.1109/tmrb.2019.2949865

Cited by 119 publications

(60 citation statements)

References 43 publications

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“…This mental process triggers the activation of the neurocognitive mechanisms that underlie the planning of voluntary movements resembling how the action is executed in a real way [ 1 ]. Currently, MI has been postulated as a reliable tool to promote motor learning in all of its phases in activities, like the development of professional motor skills learning [ 2 ], improvement of sports gestures [ 3 ], and enhancement of skilled performance in the patient rehabilitation [ 4 , 5 ], among others. In these applications, the Media and Information Literacy methodology proposed by UNESCO includes many competencies that are vital for people to be effectively engaged in human development [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects.

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Section: Introductionmentioning

confidence: 99%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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“…The electroencephalogram (EEG) signal is generally used for epileptic detection as it is a condition related to the brain's electrical activity. Analysis of EEG signal can also be adopted to fascinate advanced human robot interaction [7]- [9]. EEG recordings are recorded digitally for viewing on a computer display unit, which also lends themselves to be automatically analyzed.…”

Section: Introductionmentioning

confidence: 99%

Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High-Dimension EEG Signals

et al. 2020

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Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%. INDEX TERMS Deep learning, C-LSTM, epileptic seizure, high-dimension electroencephalogram (EEG).

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“…The active prosthesis is different from the passive prosthesis basically by the use of active components for locomotion assistance. Several research works focused on these prostheses and their control [5][6][7][8][9]. Sup et al [10] designed a transfemoral prosthesis actuated by a pneumatic cylinder and proposed an impedance-based finite-state controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

et al. 2020

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Active prosthetic knees (APKs) are widely used in the past decades. However, it is still challenging to make them more natural and controllable because: (1) most existing APKs that use rigid actuators have difficulty obtaining more natural walking; and (2) traditional finite-state impedance control has difficulty adjusting parameters for different motions and users. In this paper, a flexible APK with a compact variable stiffness actuator (VSA) is designed for obtaining more flexible bionic characteristics. The VSA joint is implemented by two motors of different sizes, which connect the knee angle and the joint stiffness. Considering the complexity of prothetic lower limb control due to unknown APK dynamics, as well as strong coupling between biological joints and prosthetic joints, an adaptive robust force/position control method is designed for generating a desired gait trajectory of the prosthesis. It can operate without the explicit model of the system dynamics and multiple tuning parameters of different gaits. The proposed model-free scheme utilizes the time-delay estimation technique, sliding mode control, and fuzzy neural network to realize finite-time convergence and gait trajectory tracking. The virtual prototype of APK was established in ADAMS as a testing platform and compared with two traditional time-delay control schemes. Some demonstrations are illustrated, which show that the proposed method has superior tracking characteristics and stronger robustness under uncertain disturbances within the trajectory error in ± 0 . 5 degrees. The VSA joint can reduce energy consumption by adjusting stiffness appropriately. Furthermore, the feasibility of this method was verified in a human–machine hybrid control model.

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Hybrid Brain/Muscle Signals Powered Wearable Walking Exoskeleton Enhancing Motor Ability in Climbing Stairs Activity

Cited by 119 publications

References 43 publications

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High-Dimension EEG Signals

Adaptive Robust Force Position Control for Flexible Active Prosthetic Knee Using Gait Trajectory

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