• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1014-1020
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• 2011

This presents a motion and force estimation system of human fingers by using an Electromyography (EMG) sensor module and a data glove system to be proposed in this paper. Both EMG sensor module and data glove system are developed in such a way to minimize the number of hardware filters in acquiring the signals as well as to reduce their sizes for the wearable. Since the onset of EMG precedes the onset of actual finger movement by dozens to hundreds milliseconds, we show that it is possible to predict the pattern of finger movement before actual movement by using the suggested system. Also, we are to suggest how to estimate the grasping force of hand based on the relationship between RMS taken EMG signal and the applied load. Finally we show the effectiveness of the suggested estimation system through several experiments.

• Physical Therapy Korea
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• v.11 no.4
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• pp.61-69
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• 2004

EMG is used in rehabilitation research to provide a method to infer muscle function. This paper will present an introduction to interpretation of electromyography (EMG) data for physical therapists. It is important for the physical therapist to have an understanding of the collection and reduction of raw electrical data from the muscle to allow the physical therapist to interpret findings in a research report, and improve planning of clinical research projects with respect to data collection. We will discuss factors that affect the type of EMG collected and the ways in which various common methods of data reduction will impact the findings from a study that uses EMG.

• Journal of the Korea Safety Management & Science
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• v.8 no.1
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• pp.43-52
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• 2006

The goal of this study is to investigate the ergonomic factors in designing or selecting the hand tool handle. Electromyogram (EMG) were measured for various wrist postures and handle sizes under two loading conditions. Anthropometric data were measured and the correlation with EMG measurement data were analyzed. Investigations of this study show that wrist posture should be neutral for minimum muscle tension and optimum handle size can be found by measuring the EMG measurement data. It show that hand width and EMG measurement data is greatly correlated also. This study can be a guide of designing or selecting a hand tool, but further study with large sample sizes and various groups is needed for making general conclusion.

• Journal of Biomedical Engineering Research
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• v.13 no.1
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• pp.1-8
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• 1992

In the field of prosthesis arm control, the pattern classification of the EMG signal is a required basis process and also the estimation of force from collected EMG data is another necessary duty. But unfortunately, what we've got is not real force but an EMG signal which contains the information of force. This is the reason why we estimate the force from the EMG data. In this paper, when we handle the EMG signal to estimate the force, spatial prewhitening process is applied from which the spatial correlation between the channels are removed. And after the orthogonal transformation which is used in the force estimation process, the transformed signal Is inputed into the probabilistic model for pattern classification. To verify the different results of the multiple channels, SNR(signal to noise ratio) function is introduced.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.194-199
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• 2016

EMG has specific information which is related to movements according to the activities of muscles. Therefore, users can intuitively control a prosthesis. For this reason, biosignals are very useful and convenient in this kind of application. Bioimpednace also provides specific information about movements like EMG. In this study, we used both EMG and bioimpedance to classify the typical hand gestures such as hand open, hand close, no motion (rest), supination, and pronation. Nine able-bodied subjects and one amputee were used as experimental data set. The accuracy was $98{\pm}1.9%$ when 2 bio-impedance and 8 EMG channels were used together for normal subjects. The number of EMG channels affected the accuracy, but it was stable when more than 5 channels were used. For the amputee, the accuracy is higher when we use both of them than when using only EMG. Therefore, accurate and stable hand motion estimation is possible by adding bioimepedance which shows structural information and EMG together.

• Physical Therapy Rehabilitation Science
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• v.10 no.3
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• pp.374-378
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• 2021

Objective: Bilateral movement training is an effective method for upper extremity rehabilitation of stroke. An approach to induce bilateral movement through functional electrical stimulation is attempted. The purpose of this study is to develop an EMG-triggered functional electrical stimulation device for upper extremity bilateral movement training in stroke patients and test its feasibility. Design: Feasibility and Pilot study design. Methods: We assessed muscle activation and kinematic data of the affected and unaffected upper extremities of a stroke patient during wrist flexion and extension with and without the device. Wireless EMG was used to evaluate muscle activity, and 12 3D infrared cameras were used to evaluate kinematic data. Results: We developed an EMG-triggered functional electrical stimulation device to enable bilateral arm training in stroke patients. A system for controlling functional electrical stimulation with signals received through a 2-channel EMG sensor was developed. The device consists of an EMG sensing unit, a functional electrical stimulation unit, and a control unit. There was asymmetry of movement between the two sides during wrist flexion and extension. With the device, the asymmetry was lowest at 60% of the threshold of the unaffected side. Conclusions: In this study, we developed an EMG-triggered FES device, and the pilot study result showed that the device reduces asymmetry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1779-1785
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• 2006

This paper presents a method of virtual robot arm control by EMG pattern recognition using the proposed hybrid system. The proposed hybrid system is composed of the LVQ and the SOFM, and the SOFM is used for the preprocessing of the LVQ. The SOFM converts the high dimensional EMG signals to 2-dimensional data. The EMG measurement system uses three surface electrodes to acquire the EMG signal from operator. Six hand gestures can be classified sufficiently by the proposed hybrid system. Experimental results are presented that show the effectiveness of the virtual robot arm control by the proposed hybrid system based classifier for the recognition of hand gestures from EMG signal patterns.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.237-244
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• 2006

This study proposes an approach to the performance improvement of EMG(Electromyogram) pattern recognition. MFCC(Mel-Frequency Cepstral Coefficients)'s approach is molded after the characteristics of the human hearing organ. While it supplies the most typical feature in frequency domain, it should be reorganized to detect the features in EMG signal. And the dynamic aspects of EMG are important for a task, such as a continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most approaches. Thus, this paper proposes reorganized MFCC and HMM-GMM, which is adaptable for the dynamic features of the signal. Moreover, it requires an analysis on the most suitable system setting fur EMG pattern recognition. To meet the requirement, this study balanced the recognition-rate against the error-rates produced by the various settings when loaming based on the EMG data for each motion.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.84.4-84
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• 2001

In this paper, we present a method to classify electromyogram (EMG) signals which are utilized to be control signals for patient-responsive walker-supported system for paraplegics. Patterns of EMG signals for dierent walking motions are classied via adequate filtering, real EMG signal extraction, AR-modeling, and modified self-organizing feature map (MSOFM). More efficient signal processing is done via a data-reducing extraction algorithm. Moreover, MSOFM classifies and determines the classified results are presented for validation.