Towards reducing the impacts of unwanted movements on identification of motion intentions

Li, Xiangxin; Chen, Shixiong; Zhang, Haoshi; Samuel, Oluwarotimi Williams; Wang, Hui; Peng, Fang; Zhang, Xiufeng; Li, Guanglin

doi:10.1016/j.jelekin.2016.03.005

Cited by 38 publications

(19 citation statements)

References 26 publications

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“…Surface electromyography (sEMG) is a kind of neural signal that contains motor commands and can be non-invasively extracted on the muscle surface of residual limbs. Due to its relative ease of acquisition and abundant content of neural information, sEMG plays an important role in the control of modern motorized prostheses [1–4] and rehabilitation robotics [4–6]. In actually applications, however, the residual muscles after amputations are usually limited, especially in the case of above-elbow amputations.…”

Section: Introductionmentioning

confidence: 99%

A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

Samuel

Zhang

et al. 2017

J NeuroEngineering Rehabil

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161

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BackgroundMost of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses.MethodsFour transhumeral amputees without any form of neurological disease were recruited in the experiments. Five motion classes including hand-open, hand-close, wrist-pronation, wrist-supination, and no-movement were specified. During the motion performances, sEMG and EEG signals were simultaneously acquired from the skin surface and scalp of the amputees, respectively. The two types of signals were independently preprocessed and then combined as a parallel control input. Four time-domain features were extracted and fed into a classifier trained by the Linear Discriminant Analysis (LDA) algorithm for motion recognition. In addition, channel selections were performed by using the Sequential Forward Selection (SFS) algorithm to optimize the performance of the proposed method.ResultsThe classification performance achieved by the fusion of sEMG and EEG signals was significantly better than that obtained by single signal source of either sEMG or EEG. An increment of more than 14% in classification accuracy was achieved when using a combination of 32-channel sEMG and 64-channel EEG. Furthermore, based on the SFS algorithm, two optimized electrode arrangements (10-channel sEMG + 10-channel EEG, 10-channel sEMG + 20-channel EEG) were obtained with classification accuracies of 84.2 and 87.0%, respectively, which were about 7.2 and 10% higher than the accuracy by using only 32-channel sEMG input.ConclusionsThis study demonstrated the feasibility of fusing sEMG and EEG signals towards improving motion classification accuracy for above-elbow amputees, which might enhance the control performances of multifunctional myoelectric prostheses in clinical application.Trial registrationThe study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077.

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Section: Introductionmentioning

confidence: 99%

A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

Samuel

Zhang

et al. 2017

J NeuroEngineering Rehabil

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161

138

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“…However, the existing EMG-PR-based upper limb prostheses are yet to be available for clinical use [15–17], and this could be mainly attributed to the gap between the academics and the industry [7, 8, 18–37]. In order to speed up the clinical implementation of the multifunctional myoelectric prostheses, some disparities between an ideal laboratory setting and practical use of a myoelectric prosthesis, such as the electrode location shift, electrode configuration, muscle contraction variation, muscle fatigue overtime, sampling rate of EMG signals, and the prosthesis weight, were investigated in different research groups worldwide [8, 18–20, 28–32, 38, 39].…”

Section: Introductionmentioning

confidence: 99%

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

Geng

Samuel

Wei

et al. 2017

BioMed Research International

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Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.

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“…e MSC signals were acquired at a sampling frequency of 1000 Hz using the fourchannel data acquisition system described above. More precisely, two different kinds of stretchable-flexible sensors, large-sized sensors (length: 8 cm and width: 0.8cm) and small-sized sensors (length: 3 cm and width: 5.0 mm), were designed for the MSC data collection with an attempt to see if the sensor size would affect the MSC recordings, as shown in Figure 3 [40][41][42]. Prior to the data collection sessions, the subjects were properly instructed about the experimental procedure to guarantee high-quality recordings.…”

Section: Setup Of the Movementsmentioning

confidence: 99%

Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction

Huang

Wang

et al. 2020

Computational and Mathematical Methods in Medicine

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Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.

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Towards reducing the impacts of unwanted movements on identification of motion intentions

Cited by 38 publications

References 26 publications

A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction

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