Classification of Hand Gestures Based on Multi-channel EMG by Scale Average Wavelet Transform and Convolutional Neural Network

Oh, Do-Chang; Jo, Yong-Un

doi:10.1007/s12555-019-0802-1

Cited by 24 publications

(6 citation statements)

References 15 publications

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“…On the other hand, deep learning (DL) algorithms such as CNNs can be employed in two ways for BCI applications: altering or modifying the CNN algorithm architecture to accommodate the one-dimensional time-series data obtained by the modalities or transforming one-dimensional data into two-dimensional (2D) data to be conveniently input to the CNN. Deep neural networks (DNNs) and other traditional classifiers have also been employed based on fNIRS and EEG signals to recognize three different cognitive states (Huve et al, 2019 ; Takahashi et al, 2021 ), electromyography signals classification (Oh and Jo, 2021 ), control of wearable exoskeleton (Sun et al, 2021 ), and other control applications (Kim et al, 2021 ; Li et al, 2021 ; Yaqub et al, 2021 ). A similar approach has been used for various other applications, such as controlling robots (Huve et al, 2018 ), differentiating workloads by analyzing the fNIRS signals, and using deep learning techniques.…”

Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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The constantly evolving human–machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain–computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

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Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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“…In [26], the authors investigated the use of sEMG signals recorded from the wrist for classification of different single-finger, multifinger and wrist gestures for HCI applications. Various studies also propose gesture classification schemes using different image representations of the sEMG signals such as Short-Time Fourier Transform (STFT) [27], Continuous Wavelet Transform (CWT) [28], [29], Emperical Mode Decoposition [30], raw sEMG images [31], sEMG muscle activation maps [32], and grayscale sEMG images [33].…”

Section: B Related Workmentioning

confidence: 99%

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

Tripathi¹,

Kumar²,

Prathosh³

et al. 2022

Preprint

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Airwriting Recognition is the task of identifying letters written in free space with finger movement. It is a dynamic gesture recognition with the vocabulary of gestures corresponding to letters in a given language. Electromyography (EMG) is a technique used to record electrical activity during muscle contraction and relaxation as a result of movement and is widely used for gesture recognition. Most of the current research in gesture recognition is focused on identifying static gestures. However, dynamic gestures are natural and userfriendly for being used as alternate input methods in Human-Computer Interaction applications. Airwriting recognition using EMG signals recorded from forearm muscles is therefore a viable solution. Since the user does not need to learn any new gestures and a large range of words can be formed by concatenating these letters, it is generalizable to a wider population. There has been limited work in recognition of airwriting using EMG signals and forms the core idea of the current work. The SurfMyoAiR dataset comprising of EMG signals recorded during writing English uppercase alphabets is constructed. Several different time-domain features to construct EMG envelope and two different timefrequency image representations: Short-Time Fourier Transform and Continuous Wavelet Transform were explored to form the input to a deep learning model for airwriting recognition. Several different deep learning architectures were exploited for this task. Additionally, the effect of various parameters such as signal length, window length and interpolation techniques on the recognition performance is comprehensively explored. The best-achieved accuracy was 78.50% and 62.19% in userdependent and independent scenarios respectively by using Short-Time Fourier Transform in conjunction with a 2D Convolutional Neural Network based classifier. Airwriting has great potential as a user-friendly modality to be used as an alternate input method in Human-Computer Interaction applications.

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“…The most common algorithms currently in use and under investigation are based on neural networks (NNs), which have become popular owing to their flexibility and strong inference performance. They have been used successfully for intention detection both individually and alongside more complex feature extraction methods over a list of prescribed motions and achieve high classification accuracy in this task [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Novel near E-Field Topography Sensor for Human–Machine Interfacing in Robotic Applications

Skoraczynski,

Chen

2024

Sensors

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This work investigates a new sensing technology for use in robotic human–machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human–machine interfaces.

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Classification of Hand Gestures Based on Multi-channel EMG by Scale Average Wavelet Transform and Convolutional Neural Network

Cited by 24 publications

References 15 publications

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

Novel near E-Field Topography Sensor for Human–Machine Interfacing in Robotic Applications

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