Evolving control of human-exoskeleton system using Gaussian process with local model

Yang, Jiantao; Peng, Cheng

doi:10.1016/j.bspc.2019.101844

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…It means the transparency of the exoskeleton was improved. In Yang and Peng, 10 an evolving learning control strategy based on Gaussian process with local model (GPLM) was proposed to realize transparent control of a human exoskeleton system. Experimental results show that the interactive force, human effort and interaction damping are reduced greatly compared with traditional force control.…”

Section: Compliance Controlmentioning

confidence: 99%

“…1–4 However, significant application breakthrough has not been achieved due to the lack of transparent human exoskeleton interaction. 5 Although extensive research has been conducted on symbiotic human exoskeleton collaborative 6,7 and many improvements have been made on compliance mechanical devices (series elastic actuator (SEA) and variable stiffness actuator (VSA)), 8 data fusion algorithms for mutual communication, 9 and control strategies for compliant motion coordination, 10 many challenges still remain. Within this context, a review and analysis on exoskeletons is needed and of great significance in developing exoskeleton technologies.…”

Section: Introductionmentioning

confidence: 99%

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A review on human intent understanding and compliance control strategies for lower limb exoskeletons

Yang

Shi

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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The exoskeleton has emerged as a promising technology to enhance humans’ strength and boost users’ efficiency. In order to provide congruent human–machine interaction for assisting the users, exoskeletons should have knowledge of human’s planned action and accordingly command the robot by the designed controller. It means the ubiquitous aspect of exoskeletons lies on motion intent understanding and active compliance control. In the last decade, extensive research has been conducted on the two topics. However, no major breakthrough has been made. Thus, a systematic review and analysis on this very subject is of great significance in developing exoskeletons. Within this context, this review first surveys the history of lower limb exoskeletons to summarize the various technologies for realizing transparent human exoskeleton coordination. Then, an overview about motion intent understanding and compliance control strategies are presented in detail. Furthermore, the future trend and research directions are also outlined.

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Section: Compliance Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review on human intent understanding and compliance control strategies for lower limb exoskeletons

Yang

Shi

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

Self Cite

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“…Thus, the effective human-machine interaction information was provided by bioelectric signals had been developed many applications in varies area. In medical area, a successful example was rehabilitation robots, which was able to improve actual movement of human-machine interaction and provide behavior prediction [1,2,3,4]. At present, bioelectric signals mainly consisted of electromyography (EMG) [5], electroencephalogram (EEG) [6] and electrooculogram (EOG) [7].…”

Section: Introductionmentioning

confidence: 99%

A novel method based on long short term memory network and discrete-time zeroing neural algorithm for upper-limb continuous estimation using sEMG signals

Chai

Liu

et al. 2021

Biomedical Signal Processing and Control

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“…For example, sEMG amplitude, root mean square (RMS), zerocrossing (ZC), autoregressive-coefficient, mean absolute value (MAV), fourier transform coefficient, cepstrumcoefficients, peak frequency, and median frequency analysis methods [14,15]. However, the time-domain or frequencydomain analysis can't completely describe the change of the sEMG signal [16]. Recent studies have shown that the timefrequency analysis can extract more sEMG feature information [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Classification of Lower Limb Motion Based on sEMG Signals

et al. 2020

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Surface electromyography (sEMG) signals can reflect the body motion information and are widely used in military, medical rehabilitation, industrial production. The lower limb motion classification mainly includes feature extraction and classification model establishment. Firstly, we proposed a feature extraction method based on the wavelet packet transform (WPT) and principal component analysis (PCA). We used the wavelet packet method to decompose the sEMG signals of three muscles in the lower limb and got the 24-dimensional eigenvector. To reduce the calculation and improve the speed of the classification model, we used the PCA method to reduce the dimension of the feature vector and got the 3-dimensional eigenvector. Then, we proposed a method based on the scale unscented Kalman filter (SUKF) and neural network (NN) for lower limb motion classification. Through the scale correction unscented transform (SCUT) could optimize the neural network weight and improve lower limb motion classification accuracy. Finally, the experimental results showed that the average accuracy was 93.7%. Compared with the backpropagation neural network (BPNN) and wavelet neural network (WNN), this method could improve the accuracy and reliability of the lower limb motion classification. INDEX TERMS sEMG signals, lower limb motion classification, feature extraction, wavelet packet transform, principal component analysis, unscented Kalman filter, neural networks.

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Evolving control of human-exoskeleton system using Gaussian process with local model

Cited by 6 publications

References 19 publications

A review on human intent understanding and compliance control strategies for lower limb exoskeletons

A review on human intent understanding and compliance control strategies for lower limb exoskeletons

A novel method based on long short term memory network and discrete-time zeroing neural algorithm for upper-limb continuous estimation using sEMG signals

Feature Extraction and Classification of Lower Limb Motion Based on sEMG Signals

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