Fractal analysis features for weak and single-channel upper-limb EMG signals

Phinyomark, Angkoon; Phukpattaranont, Pornchai; Limsakul, Chusak

doi:10.1016/j.eswa.2012.03.039

Cited by 93 publications

(64 citation statements)

References 38 publications

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“…This scaling exponent indicates the presence of fractal scaling in a detrended time series of the RMS fluctuation in a succession of random division of the integrated sEMG signal on the time domain (Phinyomark et al, 2012b). DFA offers advantage over methods based on wavelet transformations in the time-scale domain (Phinyomark et al, 2012b). On the other hand, HFD (Esteller et al, 2001) is one of the most used fractal dimension feature, as it has shown better performance than other fractal methods (Esteller et al, 2001), and has also shown good performance in the classification of EMG signals.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The mathematical definitions of DFA and HFD features are broadly described in Esteller et al (2001) and Phinyomark et al (2012b), respectively.…”

Section: Feature Extractionmentioning

confidence: 99%

“…DFA is a modified root mean square that provides a self-similarity parameter representing the fractal dimension. This scaling exponent indicates the presence of fractal scaling in a detrended time series of the RMS fluctuation in a succession of random division of the integrated sEMG signal on the time domain (Phinyomark et al, 2012b). DFA offers advantage over methods based on wavelet transformations in the time-scale domain (Phinyomark et al, 2012b).…”

Section: Feature Extractionmentioning

confidence: 99%

“…Moreover, prosthesis with electrode array are complex, in addition to the fact that differences in electrode placements lead to variations in sEMG signals and their spectrum (Kumar et al, 2013). Some works have sought for systems with low-density (less than six channels) sEMG signals Castro et al, 2015;Phinyomark et al, 2012b), which reduce problems as electrode fixation and computational demand. However, the accuracy reported by these researchers to recognize dexterous movements are still poor and their experiments were only conducted in able-bodied subjects.…”

Section: Introductionmentioning

confidence: 99%

“…Seventeen features were considered in this study, which are based on time domain (TD), frequency domain (FD) (Phinyomark et al, 2013;Zecca et al, 2002) and non-linear analysis related to Fractals (Esteller et al, 2001;Phinyomark et al, 2012b), as shown in Table 3. The mathematical definitions of DFA and HFD features are broadly described in Esteller et al (2001) and Phinyomark et al (2012b), respectively.…”

Section: Feature Extractionmentioning

confidence: 99%

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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: Feature Extractionmentioning

confidence: 99%

“…The mathematical definitions of DFA and HFD features are broadly described in Esteller et al (2001) and Phinyomark et al (2012b), respectively.…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2017

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Movement Control System for a Transradial Prosthesis Using Myoelectric Signals

Bermeo-Calderon

Velasco

Rojas

et al. 2020

Lecture Notes in Electrical Engineering

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Soft-Wearable Device for the Estimation of Shoulder Orientation and Gesture

Contreras-González

Samper-Escudero

Pont-Esteban

et al. 2020

Haptics: Science, Technology, Applications

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This study presents the development of a wearable device that merges capacitive soft-flexion and surface electromyography (sEMG) sensors for the estimation of shoulder orientation and movement, evaluating five natural movement gestures of the human arm. The use of Time Series Networks (TSN) to estimate the arm orientation, and a pattern recognition method for the estimation of the classification of the gesture are proposed. It is demonstrated that it is possible to know the orientation of the shoulder, and that the algorithm is capable of recognising the five gestures proposed with two different configurations. The study is performed on people who reported healthy upper limbs.

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Fractal analysis features for weak and single-channel upper-limb EMG signals

Cited by 93 publications

References 38 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

Movement Control System for a Transradial Prosthesis Using Myoelectric Signals

Soft-Wearable Device for the Estimation of Shoulder Orientation and Gesture

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