A wavelet-based continuous classification scheme for multifunction myoelectric control

Englehart, Kevin; Hudgin, B.; Parker, P.A.

doi:10.1109/10.914793

Cited by 613 publications

(375 citation statements)

References 10 publications

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“…Despite the system was validated in off-line mode, the required time for recording and processing the raw sEMG signals is lower than 300 ms, which agrees with the criteria reported in Englehart et al (2001) to be used in real time application. A comparison of our technique with previous works can be unbalanced because of the difference in number of electrodes and muscles selected, number of classes, whether amputees were included in the study and the kind of gestures used in relation to the level of dexterity.…”

Section: Discussionsupporting

confidence: 65%

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

et al. 2017

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Introduction: Intuitive prosthesis control is one of the most important challenges in order to reduce the user effort in learning how to use an artificial hand. This work presents the development of a novel method for pattern recognition of sEMG signals able to discriminate, in a very accurate way, dexterous hand and fingers movements using a reduced number of electrodes, which implies more confidence and usability for amputees. Methods: The system was evaluated for ten forearm amputees and the results were compared with the performance of able-bodied subjects. Multiple sEMG features based on fractal analysis (detrended fluctuation analysis and Higuchi's fractal dimension) combined with traditional magnitude-based features were analyzed. Genetic algorithms and sequential forward selection were used to select the best set of features. Support vector machine (SVM), K-nearest neighbors (KNN) and linear discriminant analysis (LDA) were analyzed to classify individual finger flexion, hand gestures and different grasps using four electrodes, performing contractions in a natural way to accomplish these tasks. Statistical significance was computed for all the methods using different set of features, for both groups of subjects (able-bodied and amputees). Results: The results showed average accuracy up to 99.2% for able-bodied subjects and 98.94% for amputees using SVM, followed very closely by KNN. However, KNN also produces a good performance, as it has a lower computational complexity, which implies an advantage for real-time applications. Conclusion:The results show that the method proposed is promising for accurately controlling dexterous prosthetic hands, providing more functionality and better acceptance for amputees.

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Section: Discussionsupporting

confidence: 65%

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

et al. 2017

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“…Each gesture was randomly repeated 10 times and recorded for 2 s. We empirically determined the best gesture duration by means of preliminary studies. Since steady-state sEMG signal are more robust than transient signal for classification purposes [19,23] repeated 10 times, a [10 × 5 × L × N] dataset matrix (from now on, we refer to it as "data") was collected for each subject. An example of raw data acquired on a whole registration session is shown in Fig.…”

Section: Data Acquisition and Experimental Protocolmentioning

confidence: 99%

“…Out of the wide variety of preprocessing EMG classification techniques being investigated [18][19][20], we chose to analyze PCA, the widely commonly used technique, and CSP, which has shown interesting potentialities in EMG pattern recognition [14]. We investigated the performances of the two approaches by using 32 combinations of features (feature vectors) and 3 different classifiers: linear discriminant analysis (LDA, linear classifier), support vector machines (SVM, non-linear kernel classifier) and artificial neural networks (ANN, non-linear classifier).…”

Section: Introductionmentioning

confidence: 99%

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

Riillo

Quitadamo

Cavrini

et al. 2014

Biomedical Signal Processing and Control

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a b s t r a c tWe propose a methodological study for the optimization of surface EMG (sEMG)-based hand gesture classification, effective to implement a human-computer interaction device for both healthy subjects and transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA) approach was compared to the promising supervised common spatial pattern (CSP) methodology to identify the best classification strategy and the related tuning parameters. A low density array of sEMG sensors was built to record the muscular activity of the forearm and classify five different hand gestures. Twenty healthy subjects were recruited to compute optimized parameters for ("within" analysis) and to compare between ("between" analysis) the two strategies. The system was also tested on a transradial amputee subject, in order to assess the robustness of the optimization in recognizing disabled users' gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accuracy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of 89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between the two strategies. Moreover we found out that the optimization computed for healthy subjects was proven to be sufficiently robust to be used on the amputee subject. This motivates further investigation of the proposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based hand gesture recognition and constitute a step toward the definition of an efficient EMG-controlled system for amputees.

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“…According to (5), we set T = 0.5 ms, T w = 128 ms, T d = 32 ms in our experiments. The selection of these parameters is weighted in [27,28]. The sampled sEMG data with respect to each patient are respectively segmented to form overlapping data sequences for healthy and diseased sides.…”

Section: Results Based On Feature Matrices and Clustering Techniquementioning

confidence: 99%

sEMG based quantitative assessment of acupuncture on Bell’s palsy: an experimental study

Han

Xiong

Zhao

et al. 2015

Sci. China Inf. Sci.

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Acupuncture is a traditional Chinese therapeutic method, recognized by western medicine as an important complementary therapy. However, the efficacy assessment of this traditional intervention has been mainly empirical rather than scientific. In this paper, we propose a surface electromyography (sEMG) based approach for quantitative assessment of acupuncture on Bell's palsy. The assessment is made by comparing the muscle activities of healthy with diseased ones. A feature-vector of four individual sEMG features is introduced for more comprehensive representation of muscle activity. A clustering technique is then proposed to calculate quantitative differences between the muscle activities of the healthy and diseased sides. A regressive model is also proposed to predict the recovery trend of a patient based on the quantitative assessments during his/her clinical acupuncture history. As reported in the paper, a total of 20 selected Bell's palsy patients have participated in the extensive experiments that were conducted to verify the performance of the proposed method.

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A wavelet-based continuous classification scheme for multifunction myoelectric control

Cited by 613 publications

References 10 publications

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

Dexterous hand gestures recognition based on low-density sEMG signals for upper-limb forearm amputees

Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

sEMG based quantitative assessment of acupuncture on Bell’s palsy: an experimental study

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