A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control

Chu, Jun-Uk; Moon, Inhyuk; Lee, Yun‐Jung; Kim, Shin-Ki; Mun, Mingyon

doi:10.1109/tmech.2007.897262

Cited by 262 publications

(177 citation statements)

References 20 publications

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“…In research that targets estimating hand state from sEMG, many studies have focused on discriminating hand patterns [29][30][31][32][33][34]; that is, they deal with discrete clustering problems using sEMG as input information. The resultant systems can recognize hand shape (e.g., "open", "fist," or "peace sign"), and the main aim now is to apply such systems to the problem of controlling a prosthetic hand.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

Hioki

Kawasaki

2012

ISRN Rehabilitation

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Background. The surface electromyogram (sEMG) is strongly related to human motion and is useful as a human interface in robotics and rehabilitation. The purpose of this study was to establish a new system for estimating finger joint angles using few sEMG channels. Methods. To deal with a dynamic system, the proposed method adopts time delay factors and a feedback stream into a neural network (NN) with 6 system parameters. The 2 target motion patterns were each tested with 5 subjects. 1000 combinations of system parameter sets were tested. Results. A system with only 4 channels can estimate angles with 7.1-11.8% root mean square (RMS) error, which is approximately the same level of accuracy achieved by other systems using 15 channels. Conclusions. The use of so few channels is a great advantage in an sEMG system because it provides a convenient interface system. This advantage is conferred by the proposed NN system.

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Section: Introductionmentioning

confidence: 99%

Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

Hioki

Kawasaki

2012

ISRN Rehabilitation

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“…There are many methods applied in EMG classification which mostly work based on pattern recognition such as Neural Networks, Fuzzy, Neuro-Fuzzy, Probabilistic and Online training. Many literatures emphasize the success of neural networks in myoelectric classification where MLP is used to classify time domain features (Lamounie et al, 2003), LDA performed better with time-scale features (Chu et al, 2007) and Lamounier et al (2002) applied RBF for their purpose. There are many efforts in fuzzy approach (Ajiboye and Weir, 2005) and Evidence Accumulation (EA) method was applied by Gazzoni et al, (2004).…”

Section: Introductionmentioning

confidence: 99%

Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

Hamedi

Salleh²,

Swee³

et al. 2011

Journal of Computer Science

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Problem statement: Facial expression recognition has been improved recently and it has become a significant issue in diagnostic and medical fields, particularly in the areas of assistive technology and rehabilitation. Apart from their usefulness, there are some problems in their applications like peripheral conditions, lightening, contrast and quality of video and images. Approach: Facial Action Coding System (FACS) and some other methods based on images or videos were applied. This study proposed two methods for recognizing 8 different facial expressions such as natural (rest), happiness in three conditions, anger, rage, gesturing 'a' like in apple word and gesturing no by pulling up the eyebrows based on Three-channels in Bi-polar configuration by SEMG. Raw signals were processed in three main steps (filtration, feature extraction and active features selection) sequentially. Processed data was fed into Support Vector Machine and Fuzzy C-Means classifiers for being classified into 8 facial expression groups. Results: 91.8 and 80.4% recognition ratio had been achieved for FCM and SVM respectively. Conclusion: The confirmed enough accuracy and power in this field of study and FCM showed its better ability and performance in comparison with SVM. It's expected that in near future, new approaches in the frequency bandwidth of each facial gesture will provide better results.

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“…Rates as high as 97.4% EMG based recognition accuracy have been achieved (Chu, et al, 2007). While this is a major achievement in upper extremity control, in highly fault-intolerant tasks such as walking, a 2.6% error rate could prove disastrous.…”

Section: Lower Extremity Prostheticsmentioning

confidence: 99%

Man to Machine, Applications in Electromyography

Wehner¹

2012

EMG Methods for Evaluating Muscle and Nerve Function

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A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control

Cited by 262 publications

References 20 publications

Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

Man to Machine, Applications in Electromyography

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