A comparison of waveform fractal dimension algorithms

Esteller, Rosana; Vachtsevanos, George; Echauz, Javier; Litt, Brian

doi:10.1109/81.904882

Cited by 400 publications

(275 citation statements)

References 13 publications

(20 reference statements)

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“…For ZC and SSC, the thresholds values used were 0.0005 and 0.001, respectively. Other parameters were chosen according to the literature, such as: for AR (order = 4), DFA (L = 10), PSR (N = 20) (Phinyomark et al, 2013); and HFD (Kmax = 10) (Esteller et al, 2001). Additionally, the classifier's parameters were optimized as: for KNN (K = 9) and for SVM (C = 0.01 and polynomial kernel with order 3).…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…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: Resultsmentioning

confidence: 99%

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%

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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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“…Fractal dimension was calculated using the procedure reported by Higuchi [8] for irregular time series. This procedure yields a more accurate estimation of fractal dimension [12].…”

Section: Methods Two: Identification Of Hand Gestures Using Semg Recormentioning

confidence: 99%