A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study

Shi, Wan-Ting; Lyu, Zong-Jhe; Tang, Shih‐Tsang; Chia, Tsorng-Lin; Yang, Chia‐Yen

doi:10.1016/j.bbe.2017.11.001

Cited by 141 publications

(65 citation statements)

References 25 publications

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“…Obecnie w pracach naukowych zauważyć można niesłabnące zainteresowanie sygnałami EMG. Prowadzone badania mają na celu wykrywanie gestów wykonanych dłonią [1][2][3], pomoc sportowcom [4,5], jak również wiele innych [6,7]. Sygnał może być pobierany zarówno z powierzchni skóry, jak i z wnętrza mięśnia [8], a rejestrowana liczba sygnałów może być różna -od 1 do 16 i więcej [1,9,10].…”

Section: Wprowadzenieunclassified

“…Między systemami opisanymi w literaturze [1][2][3]9] oraz systemem, którego dotyczy niniejsza praca dostrzec można pewną różnicę odnośnie głównych założeń. Omawiany układ stworzony został w celu rozpoznawania ruchu pojedynczych palcównie gestów wykonywanych dłonią.…”

Section: Wprowadzenieunclassified

“…W dziedzinie czasu mogą to być m.in. takie parametry jak: − średnia z wartości bezwzględnej amplitudy sygnału w zadanym oknie czasowym, − liczba zmian znaku wartości sygnału EMG, − liczba zmian znaku pochodnej wartości sygnału EMG [1,2].…”

Section: Rys 8 Przykład Sygnału Emg Po Przejściu Przez Filtr Iir 10unclassified

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Recognition of Finger Movement Based on Electromyogram Analysis

Sikora

Paszkiel

2018

PAR

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Section: Wprowadzenieunclassified

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Recognition of Finger Movement Based on Electromyogram Analysis

Sikora

Paszkiel

2018

PAR

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“…Surface EMG is a non-invasive method that records the bioelectric signal generated by muscle activation. Current technologies have allowed the use of EMG pattern recognition to a broad range of applications such as man-machine interface, robot assisted rehabilitation device, and myoelectric prosthetic [1]. However, EMG signals are non-linear and non-stationary due to their complex nature.…”

Section: Introductionmentioning

confidence: 99%

EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

et al. 2019

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Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications.

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“…The EMG signal is a non-stationary random signal and usually requires noise reduction and signal amplification [2]. The development of EMG signal recognition systems has become a research hotspot [3][4][5][6][7]. However, in these studies, there are few applications related to forestry machinery.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

Wang

et al. 2019

Applied Sciences

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It is necessary to complete the two parts of gesture recognition and wireless remote control to realize the gesture control of the automatic pruning machine. To realize gesture recognition, in this paper, we have carried out the research of gesture recognition technology based on surface electromyography signal, and discussed the influence of different numbers and different gesture combinations on the optimal size. We have calculated the 630-dimensional eigenvector from the benchmark scientific database of sEMG signals and extracted the features using principal component analysis (PCA). Discriminant analysis (DA) has been used to compare the processing effects of each feature extraction method. The experimental results have shown that the recognition rate of four gestures can reach 100.0%, the recognition rate of six gestures can reach 98.29%, and the optimal size is 516~523 dimensions. This study lays a foundation for the follow-up work of the pruning machine gesture control, and p rovides a compelling new way to promote the creative and human computer interaction process of forestry machinery.

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A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study

Cited by 141 publications

References 25 publications

Recognition of Finger Movement Based on Electromyogram Analysis

Recognition of Finger Movement Based on Electromyogram Analysis

EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

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