Deep Convolutional Neural Network for Featureless Electromyogram Pattern Recognition Using Time-Frequency Distribution

Too, Jingwei; Abdullah, Abdul Rahim; Saad, Norhashimah Mohd; Ali, Nursabillilah Mohd; Zawawi, Tengku Nor Shuhada Tengku

doi:10.1166/sl.2018.3926

Cited by 5 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Too et al [22] used the pre-processed EMG data to feed to the classifier directly without feature extraction, this process can reduce computational cost. However, extracting suitable features from EMG signals can enhance the inherent properties of EMG signals.…”

Section:  Issn: 2252-8938mentioning

confidence: 99%

Short-term hand gestures recognition based on electromyography signals

Essa

Jaber

Jasim

2023

IJ-AI

View full text Add to dashboard Cite

Electromyography pattern recognition to predict limb movements can significantly enhance the control of the prosthesis. However, this technique has not yet been widely used in clinical practice. Improvements in the myoelectric pattern recognition (MPR) system can improve the functionality of the prosthesis. This study proposes new sets of time domain features to enhance the MPR control system. Three groups of features are evaluated, time domain with auto regression (TD-AR), frequency domain (FD), and timefrequency domain (TFD). The electromyography signals (EMG) are obtained from the Ninapro database-5 (DB5), a publicly available dataset for hand prosthetics. The long-term signals of DB5 are divided into short-term signals to perform short-term signals recognition. The three feature sets are extracted from the short-term signals. The results showed that the performance of the proposed TD-AR features outperformed that of the FD and TFD feature sets. The TD-AR-based discrimination performance of 40 gestures achieved a precision of 88.8% and a sensitivity of 82.6%. The integration of short-term identification with reliable features can improve classification accuracy even for a large number of gestures. A comparison with the latest works shows the reliability of the proposed work.

show abstract

Section:  Issn: 2252-8938mentioning

confidence: 99%

Short-term hand gestures recognition based on electromyography signals

Essa

Jaber

Jasim

2023

IJ-AI

View full text Add to dashboard Cite

show abstract

“…To facilitate real-time processing, we use the MYO armband (Thalmic Labs, Waterloo, Canada) to acquisition of sEMG data [32]. The MYO armband is composed of eight sEMG dry sensors (as shown in Figure 1).…”

Section: Sensors and Data Acquisitionmentioning

confidence: 99%

“…However, most of them have to wait for the completion of the gestures and cannot be applied in a real-time system. Moreover, some researchers have investigated the featureless approach that directly feeds the preprocessed data without feature extraction into a classifier, a process which may reduce the computational cost [31,32]. However, extracting appropriate features from processed data can strengthen the inherent characteristics of the sEMG signal, of which the feature selection is the key to improve the discrimination performance.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

Zhang

Yang

Qian

et al. 2019

Sensors

130

View full text Add to dashboard Cite

In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.

show abstract

“…The armband can recognize five kinds of hand movements, including palm extension, making a fist, turning wrist outwards, turning wrist inwards, thumb and middle finger two consecutive clicking. This sEMG sensor is equipped with eight medical grade stainless steel dry electrodes that can achieve eight-channel sEMG data acquisition [3] [4]. This device is easy to use.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Subtle Gestures by 2-Channel sEMG Using Parameter Estimation Classifiers Based on Probability Density

Liang

Chen

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The abundant movement information carried by the surface electromyography (sEMG) signal can comprehensively reflect the muscle movement patterns on the surface of the human body, and the characteristics of physiological signals involving movement information have promoted the vigorous development in the field of human-computer interaction in machine learning. This paper takes subtle gestures as the research objects, and proposes a subtle gestures recognition system that uses two-channel sEMG signal and utilizes Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) which are parameter estimation classifiers based on probability density. First, combining the raw sEMG signal and the envelope sEMG signal, the number of channels for sensors and corresponding position of the muscle groups are preferably selected. Then, we propose to use the Pearson correlation coefficient to optimize the four types of features extracted in the time domain, frequency domain, time-frequency domain and AR model parameters, which effectively ensures the uniqueness of each gesture and eliminates individual differences. Finally, this paper compares four classifiers of QDA, LDA, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), and explores the pattern recognition classifier suitable for subtle actions. The experiment collects 9 kinds of subtle gestures from 8 subjects. The results show that the system can use only two channels of sEMG signal to recognize 9 kinds of subtle gestures which include multifinger clicking or pinching or holding objects, and the most suitable classifiers QDA and LDA have the average recognition rates of 95.79% and 95.01%, respectively.

show abstract

Deep Convolutional Neural Network for Featureless Electromyogram Pattern Recognition Using Time-Frequency Distribution

Cited by 5 publications

References 0 publications

Short-term hand gestures recognition based on electromyography signals

Short-term hand gestures recognition based on electromyography signals

Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

Recognition of Subtle Gestures by 2-Channel sEMG Using Parameter Estimation Classifiers Based on Probability Density

Contact Info

Product

Resources

About