Critical Exponent Analysis Applied to Surface EMG Signals for Gesture Recognition

Phinyomark, Angkoon; Phothisonothai, Montri; Phukpattaranont, Pornchai; Limsakul, Chusak

doi:10.2478/v10178-011-0061-9

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…Among existing approaches, machine learning-based approach is very popular and successful ( Qi et al, 2020 ; Wong et al, 2021 ). For example, Phinyomark et al (2011) applied the critical index analysis and fractal dimension to extract the characteristics of surface EMG signals, and seven kinds of gestures were recognized from eight-channel EMG signals. Ishii et al (2012) divided hand motions into six movements and classified finger motions using two types of characteristics.…”

Section: Introductionmentioning

confidence: 99%

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

Cao

Cai

et al. 2021

Front. Comput. Neurosci.

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Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.

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Section: Introductionmentioning

confidence: 99%

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

Cao

Cai

et al. 2021

Front. Comput. Neurosci.

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“…At present, the common feature extraction methods applied to raw sEMG signal are feature extraction methods based on time-domain statistical features, frequency-domain statistical features, timefrequency domain statistical features, and parametric model [11]. Time-domain features extract time structures in the sEMG signal [16][17][18][19]. Time-domain statistical features can intuitively reflect the amplitude characteristics of the sEMG signal, and its algorithm is easy to implement.…”

Section: Feature Extractionmentioning

confidence: 99%

Gesture recognition method based on a single-channel sEMG envelope signal

Liang

Zhang

et al. 2018

J Wireless Com Network

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In the past, investigators tend to use multi-channel surface electromyography (sEMG) signal acquisition devices to improve the recognition accuracy for the study of gesture recognition systems based on sEMG. The disadvantages of the method are the increased complexity and the problems such as signal crosstalk. This paper explores a gesture recognition method based on a single-channel sEMG envelope signal feature in the time domain. First, we get the sEMG envelope signal by using a preprocessing circuit. Then, we use the improved method of valid activity segment extraction to find every valid activity segment and extract 15 features from every valid activity segment. Next, we calculate the absolute value of the correlation coefficient between each of the features and target values. After removing the feature with the smaller correlation coefficient, we reserve the 14 features. By the PCA dimensionality reduction algorithm, we transform the 14-dimensional feature into 2-dimensional feature space. Finally, we use the improved KNN algorithm and the soft margin SVM algorithm to complete the classification of five types of gestures. We obtain the gesture recognition rates of 75.8 and 79.4% by using the improved KNN algorithm and the soft margin SVM algorithm.

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“…Fractal dimension of surface EMG signals is found under different types and levels of muscle contraction using several fractal methods, e.g. box-counting method (Gupta, Suryanarayanan, & Reddy, 1997), correlation dimension method (Hu, Wang, & Ren, 2005), critical exponent analysis (Phinyomark, Phothisonothai, Phukpattaranont, & Limsakul, 2011b), and Katz method (Gitter & Czerniecki, 1995). Based on the finding in previous studies, a fractal dimension of EMG signals depends on the level of muscle contraction during strong or high level activities (Gupta et al, 1997;Hu et al, 2005).…”

Section: Feature Set 2: Low-level and High-level Surface Emg Signalsmentioning

confidence: 99%

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

Phinyomark

Quaine

Laurillau

Advances in Medical Technologies and Clinical Practice

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Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.

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Critical Exponent Analysis Applied to Surface EMG Signals for Gesture Recognition

Cited by 11 publications

References 22 publications

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

Gesture recognition method based on a single-channel sEMG envelope signal

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

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