Prostheses Control with Combined Near-Infrared and Myoelectric Signals

Herrmann, Stefan; Attenberger, Andreas; Buchenrieder, Klaus

doi:10.1007/978-3-642-27579-1_77

Cited by 28 publications

(25 citation statements)

References 14 publications

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“…A method based on the combination of sEMG and near-infrared (NIR) signals was reported [15], where hand gestures could be identified by adjoining two or more sEMG/NIR sensors. However, the NIR signals are associated to limb movements and thus still depend on the amputation condition.…”

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

164

138

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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

164

138

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“…As the LED is only switched on periodically, a NIRS feature taking into account the pulse rate and duty cycle can be derived from the sensors' NIR signals [4]. For the vectors n = {n 1 , ..., n k }, consisting of the measured NIR signal in the observed time frame, and e = {e 1 , ..., e k }, with Ena(e i ) denoting the state of the enable signal (either 0 or 1 depending on an upper and lower threshold) at each point of time in the signal window, the NIRS feature can be calculated as follows:…”

Section: A Data Acquisition and Feature Extractionmentioning

confidence: 99%

“…Recently, research about a novel type of sensor using nearinfrared (NIR) light, to detect muscle activity, has been disclosed [3] [4]. Near-infrared light is partially absorbed by the hemoglobin in the red blood cells.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Visualization of Classification-Based Control Schemes for Upper Limb Prostheses

Attenberger

Buchenrieder

2012

2012 IEEE 19th International Conference and Workshops on Engineering of Computer-Based Systems

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During the development of control schemes for upper-limb prostheses, the selection of a classification method is the decisive factor on predicting the correct hand movements. This contribution brings forward an approach to validate and visualize the output of a chosen classifier by simulative means. Using features extracted from a collection of recorded myoelectric signals (MES), a first training set for five different classes of hand movements is produced. Subsequentially, additional MES recordings are deployed to validate the classifier. By using the output for controlling the 3D model of a prosthetic hand, the behavior of an actual prosthesis is simulated and the results of the simulation visualized. For systematic comparison and selection of different classification methods, as well as extending the number of possible motion classes, a toolbox for MATLAB TM is currently developed. By employing these tools, data from sensors combining near-infrared (NIR) spectroscopy with electromyography (EMG) can be integrated into the classification process. Our classification results show, that existing classification schemes based on EMG data can be improved significantly by adding NIR sensor data. Employing only two combined EMG-NIR sensors, five motion classes comprising full movements, including pronation and supination, can be distinguished with 100% accuracy.

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“…Several physiological phenomena have been reported as responsible for the muscle optical changes, including blood perfusion [91,173], muscle shortening [92], and muscle fiber direction [93]. Numerous studies show that NIRS signals are qualitatively similar to EMG signals in both magnitude and duration for different levels of contraction [91,92,94], with the particular difference that isometric and isotonic contractions can be distinguished from NIRS signals [93].…”

Section: Muscle Vibrationmentioning

confidence: 99%

“…Numerous studies show that NIRS signals are qualitatively similar to EMG signals in both magnitude and duration for different levels of contraction [91,92,94], with the particular difference that isometric and isotonic contractions can be distinguished from NIRS signals [93]. Furthermore, Herrmann et al [173] proposed to combine EMG and NIRS signals to improve the pattern classification algorithm for the control of a prosthetic hand.…”

Section: Muscle Vibrationmentioning

confidence: 99%

Control interfaces to actively support the arm function of men with Duchenn Muscular Dystrophy

Lobo-Prat¹

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Prostheses Control with Combined Near-Infrared and Myoelectric Signals

Cited by 28 publications

References 14 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

Modeling and Visualization of Classification-Based Control Schemes for Upper Limb Prostheses

Control interfaces to actively support the arm function of men with Duchenn Muscular Dystrophy

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