Real-Time Hand Gesture Classification Using CRNN With Scale Average Wavelet Transform

Jo, Yong-Un; Oh, Do-Chang

doi:10.1142/s021951942040028x

Cited by 7 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The FFNN, LSTM, RNN, and GRU models exhibited equivalent accuracy, 95% for the DualMyo dataset and 91% for the NinaPro DB5 dataset. Jo et al [48] suggested Convolutional Recurrent Neural Network (CRNN) model to recognize hand motions in real time by combining LSTM for time-series information classification and CNN for feature extraction. Two grips, three hand signals, and one rest are recognized and categorised as hand gestures.…”

Section: Review Of Related Approachesmentioning

confidence: 99%

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

et al. 2023

View full text Add to dashboard Cite

One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect electromyographic (EMG) data from the forearm’s flexion and extension muscles. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Eight EMG sensors were used to capture each contraction of the arm muscles during one of the seven actions. Feature selection was performed using the Pareto front. Our system was able to reconstruct the kinematics of hand/finger movement and simulate the behaviour of every motion pattern. Analysis has revealed that gesture categories substantially overlap in the feature space. The correlation of the computed joint trajectories based on EMG and the monitored hand movement was 0.96 on average. Moreover, statistical research conducted on various machine learning setups revealed a 92% accuracy in measuring the precision of finger motion patterns.

show abstract

Section: Review Of Related Approachesmentioning

confidence: 99%

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The dataset EMG signals similar to five diverse gestures are formed as part of the study. In Jo and Oh (2020), the authors aimed to vigorously detect hand gestures in real time by employing CRNN with overlapping windows and preprocessing. CRNN is a deep learning method that incorporates LSTM for time-sequence data classification and CNN for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Design of Information Feedback Firefly Algorithm with a Nested Deep Learning Model for Intelligent Gesture Recognition of Visually Disabled People

Aldehim,

Marzouk,

Al-Hagery

et al. 2023

Journal of Disability Research

View full text Add to dashboard Cite

Gesture recognition is a developing topic in current technologies. The focus is to detect human gestures by utilizing mathematical methods for human–computer interaction. Some modes of human–computer interaction are touch screens, keyboard, mouse, etc. All these gadgets have their merits and demerits while implementing versatile hardware in computers. Gesture detection is one of the vital methods to construct user-friendly interfaces. Generally, gestures are created from any bodily state or motion but typically originate from the hand or face. Therefore, this manuscript designs an Information Feedback Firefly Algorithm with Nested Deep Learning (IFBFFA-NDL) model for intelligent gesture recognition of visually disabled people. The presented IFBFFA-NDL technique exploits the concepts of DL with a metaheuristic hyperparameter tuning strategy for the recognition process. To generate a collection of feature vectors, the IFBFFA-NDL technique uses the NASNet model. For optimal hyperparameter selection of the NASNet model, the IFBFFA algorithm is used. To recognize different types of gestures, a nested long short-term memory classification model was used. For exhibiting the improvised gesture detection efficiency of the IFBFFA-NDL technique, a detailed comparative result analysis was conducted and the outcomes highlighted the improved recognition rate of the IFBFFA-NDL technique as 99.73% compared to recent approaches.

show abstract

“…The sEMG signal is an unstable bioelectric signal with different frequency components at different moments [ 24 ]. The representative time-frequency analysis methods of the sEMG signal include fast Fourier transform (FFT) [ 25 ], short-term Fourier transform (STFT) [ 26 , 27 ], Wigner–Ville distribution (WVD) [ 28 , 29 ] and Hilbert–Huang transform (HHT) [ 30 ], and wavelet transform (WT) [ 18 , 31 , 32 ]. The prerequisite for FFT and STFT to effectively analyze the signal is that the signal is stable [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

Huo

Qin

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

As an input method of signal language, the hand movement classification technology has developed into one of the ways of natural human-computer interaction. The surface electromyogram (sEMG) signal contains abundant human movement information and has significant advantages as the input signal of human-computer interaction. However, how to effectively extract components from sEMG signals to improve the accuracy of hand motion classification is a difficult problem. Therefore, this work proposes a novel method based on wavelet packet transform (WPT) and principal component analysis (PCA) to classify six kinds of hand motions. The method applies WPT to decompose the sEMG signal into multiple sub-band signals. To efficiently extract the intrinsic components of the sEMG signal, the classification performance of different wavelet packet basis functions is evaluated. The PCA algorithm is used to reduce the dimension of the feature space composed of the features reflecting hand motions extracted from each sub-band signal. Besides, to ensure higher classification performance while reducing the dimension of the feature space by the PCA algorithm, the classification performance of different dimensions of the feature space is compared. In addition, the effects of the variability of the sEMG signal and the size of the window on the proposed method are further analyzed. The proposed method was tested on the sEMG for Basic Hand Movements Data Set and achieved an average accuracy of 96.03%. Compared with the existing research, the proposed method has better classification performance, which indicates that the research results can be applied to the fields of exoskeleton robot, rehabilitation training, and intelligent prosthesis.

show abstract

Real-Time Hand Gesture Classification Using CRNN With Scale Average Wavelet Transform

Cited by 7 publications

References 14 publications

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

Design of Information Feedback Firefly Algorithm with a Nested Deep Learning Model for Intelligent Gesture Recognition of Visually Disabled People

A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

Contact Info

Product

Resources

About