Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

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Techniques of EMG signal analysis: detection, processing, classification and applications

A speech signal processing system comprises an audio processor (103) for providing a first signal representing an acoustic speech signal of a speaker. An EMG processor (109) provides a second signal which represents an electromyographic signal for the speaker captured simultaneously with the acoustic speech signal. A speech processor (105) is arranged to process the first signal in response to the second signal to generate a modified speech signal. The processing may for example be a beam forming, noise compensation, or speech encoding. Improved speech processing may be achieved in particular in an acoustically noisy environment.

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Speech signal processing

This paper presents a framework for hand gesture recognition based on the information fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) sensors. In our framework, the start and end points of meaningful gesture segments are detected automatically by the intensity of the EMG signals. A decision tree and multistream hidden Markov models are utilized as decision-level fusion to get the final results. For sign language recognition (SLR), experimental results on the classification of 72 Chinese Sign Language (CSL) words demonstrate the complementary functionality of the ACC and EMG sensors and the effectiveness of our framework. Additionally, the recognition of 40 CSL sentences is implemented to evaluate our framework for continuous SLR. For gesture-based control, a real-time interactive system is built as a virtual Rubik's cube game using 18 kinds of hand gestures as control commands. While ten subjects play the game, the performance is also examined in user-specific and user-independent classification. Our proposed framework facilitates intelligent and natural control in gesture-based interaction.

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A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors

This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006. It was brought to the attention of the journal and authors that reference 74 was incorrect. The original citation for reference 74, “Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99–103.” should read “Costanza E, Inverso SA, Allen R. ‘Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller’ in Proc CHI2005, April 2005, Portland, OR, USA.”

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Techniques of EMG signal analysis: detection, processing, classification and applications (Correction).

A mobile terminal and a control method thereof, which can obtain an image, are provided. A mobile terminal (100) includes a camera unit (121), a pictogram extraction unit (182) and a controller (180). The camera unit (121) obtains image information corresponding to at least one of a still image and a moving image. The pictogram extraction unit (182) extracts at least one pictogram from the obtained image information. The controller (180) detects information related to the extracted pictogram, and displays the detected information to be overlapped with the obtained image information. In the mobile terminal, the controller (180) includes, as the detected information, at least one of previously recorded information and currently searched information related to the extracted pictogram.

Mobile terminal and control method thereof

We propose unvoiced speech recognition, "Mime Speech Recognition" It recognizes speech by observing the muscles associated with speech It is not based on voice signals but electromyography (EMG) It will realize unvoiced communication, which is a new communication style Because voice signals are not used, it can be applied in noisy environments; it also supports people without vocal-cords and aphasics In preliminary experiments, we try to recognize the 5 Japanese vowels EMG signals from the 3 muscles that contribute greatly to the utterance of Japanese vowels are input to a neural network The recognition accuracy is over 90% for the three subjects tested

"Unvoiced speech recognition using EMG - mime speech recognition"

A headphone-type gaze detector for a full-time wearable interface is proposed. It uses a Kalman filter to analyze multiple channels of EOG signals measured at the locations of headphone cushions to estimate gaze direction. Evaluations show that the average estimation error is 4.4® (horizontal) and 8.3® (vertical), and that the drift is suppressed to the same level as in ordinary EOG. The method is especially robust against signal anomalies. Selecting a real object from among many surrounding ones is one possible application of this headphone gaze detector.

Full-time wearable headphone-type gaze detector

A technique for improving the recognition accuracy of EMG-based speech recognition by applying existing speech recognition technologies is proposed. The authors have proposed an EMG-based speech recognition system that requires only mouth movements, voice need not be generated. A multi-stream HMM (hidden Markov model) and feature extraction technique are applied to EMG-based speech recognition. 3 channel facial EMG signals are collected from ten subjects when uttering 10 Japanese isolated digits. One channel corresponds to one stream. By examining various features, we found that the delta component of the static parameter leads to higher accuracy. Compared to equal stream weighting, the individual optimization of stream weights increased recognition accuracy by 4.0% which corresponds to a 12.8% reduction in error rate. This result shows that multistream HMM is effective for the classification of EMG.

Multi-stream HMM for EMG-based speech recognition

Electrooculography (EOG) is one of the measures used to estimate the direction of a person's gaze; however, conventional EOG techniques suffer from a drift issue which makes it difficult to extract an accurate absolute eye angle. The technique proposed here is based on the nonlinearity of the EOG and offers a practical solution to this problem. It estimates the absolute eye angles before and after a saccade, which cancels the offset due to the drift. Additionally, it does not require any effort from the user or any target, but instead uses only the difference of the EOGs. Experiments with five subjects confirm that the proposed technique can estimate the absolute eye angle with an error of less than 4(°). They also show improvements are achieved with several options such as weighting and multiple saccades. The technique will contribute to practical EOG-based interaction systems.

Direct Gaze Estimation Based on Nonlinearity of EOG

The object of the present invention is to keep a high success rate in recognition with a low-volume of sound signal, without being affected by noise. The speech recognition system comprises a sound signal processor 10 configured to acquire a sound signal, and to calculate a sound signal parameter based on the acquired sound signal; an electromyographic signal processor 13 configured to acquire potential changes on a surface of the object as an electromyographic signal, and to calculate an electromyographic signal parameter based on the acquired electromyographic signal; an image information processor 16 configured to acquire image information by taking an image of the object, and to calculate an image information parameter based on the acquired image information; a speech recognizer 20 configured to recognize a speech signal vocalized by the object, based on the sound signal parameter, the electromyographic signal parameter and the image information parameter; and a recognition result provider 21 configured to provide a result recognized by the speech recognizer 20.

Hiroyuki Manabe

Papers

"Unvoiced speech recognition using EMG - mime speech recognition"

Full-time wearable headphone-type gaze detector

Multi-stream HMM for EMG-based speech recognition

Direct Gaze Estimation Based on Nonlinearity of EOG

Speech recognition system, speech recognition method, speech synthesis system, speech synthesis method, and program product