A cepstrum analysis-based classification method for hand movement surface EMG signals

2019 ◽  
Vol 57 (10) ◽  
pp. 2179-2201 ◽  
Author(s):  
Erdem Yavuz ◽  
Can Eyupoglu
Keyword(s):  
Hand Movement ◽  
Surface Emg ◽  
Classification Method ◽  
Cepstrum Analysis

scholarly journals Swallowing Pattern Classification Method Using Multichannel Surface EMG Signals of Suprahyoid and Infrahyoid Muscles

2020 ◽  
Vol 9 (0) ◽  
pp. 10-20
Author(s):  
Masahiro Suzuki ◽  
Makoto Sasaki ◽  
Katsuhiro Kamata ◽  
Atsushi Nakayama ◽  
Isamu Shibamoto ◽  
...  
Keyword(s):  
Pattern Classification ◽  
Surface Emg ◽  
Classification Method

scholarly journals Methodology Proposal of EMG Hand Movement Classification Based on Cross Recurrence Plots

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
M. A. Aceves-Fernandez ◽  
J. M. Ramos-Arreguin ◽  
E. Gorrostieta-Hurtado ◽  
J. C. Pedraza-Ortega
Keyword(s):  
Hand Movement ◽  
Surface Emg ◽  
Noisy Environment ◽  
Recurrence Plots ◽  
Machine Learning Methods ◽  
Nonlinear Features ◽  
Quantification Analysis ◽  
Cross Recurrence Quantification ◽  
Cross Recurrence Quantification Analysis ◽  
Semg Signals

Dealing with electromyography (EMG) signals is often not simple. The nature of these signals is nonstationary, noisy, and high dimensional. These EMG characteristics make their predictability even more challenging. Cross recurrence plots (CRPs) have demonstrated in many works their capability of detecting very subtle patterns in signals often buried in a noisy environment. In this contribution, fifty subjects performed ten different hand movements with each hand with the aid of electrodes placed in each arm. Furthermore, the nonlinear features of each subject’s signals using cross recurrence quantification analysis (CRQA) have been performed. Also, a novel methodology is proposed using CRQA as the mainstream technique to detect and classify each of the movements presented in this study. Additional tools were presented to determine to which extent this proposed methodology is able to avoid false classifications, thus demonstrating that this methodology is feasible to classify surface EMG (SEMG) signals with good accuracy, sensitivity, and specificity. Lastly, the results were compared with traditional machine learning methods, and the advantages of using the proposed methodology above such methods are highlighted.

scholarly journals Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiwen Yang ◽  
Du Jiang ◽  
Ying Sun ◽  
Bo Tao ◽  
Xiliang Tong ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Hand Movement ◽  
Surface Emg ◽  
Residual Network ◽  
Semg Signal ◽  
Essential Information ◽  
Emg Signal ◽  
Dynamic Gesture Recognition ◽  
Computer Interaction

Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.

Novel Rehabilitation Devices for Hand Movement Disorders

2011 ◽  
pp. 312-318
Author(s):  
Akira Gyoten ◽  
Jinglong Wu ◽  
Satoshi Takahashi
Keyword(s):  
Movement Disorders ◽  
Hand Movement ◽  
Surface Emg ◽  
Portable Device ◽  
Recovery Effect ◽  
Stroke Patients ◽  
Hand Motion ◽  
Rehabilitation Devices ◽  
At Home

Numerous therapeutic rehabilitation devices have been studied. This chapter describes novel rehabilitation devices designed to treat hand movement disorders. Recently, robot-aided rehabilitation using instruments, such as a hand motion robots and a robotic glove, have attracted interest because they help recover motor function in stroke patients. The lack of proper care for at-home patients is a major problem. The authors of this chapter developed a novel portable device, consisting of two grips, that allows the patient to perform exercises at home. While a patient grasps both grips with one hand, the driving grip reciprocates at several speed adjustments. The relative distance between the movable and fixed grip enables the hand to open. In addition, a master-slave system that measures the surface EMG on the healthy arm is proposed for self-controlled rehabilitation therapy. This portable device is not complex and can be used without assistance. Future development will improve the quality of the system, and the recovery effect will be evaluated in clinical trials.

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