• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.2
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• pp.143-152
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• 2000

As muscular contraction is sustained, the Fourier spectrum of the myoelectric signal is shifted toward the lower frequency. This spectral density is associated with muscle fatigue. This paper describes a quantitative measurement method that performs the measurement of localized muscle fatigue by tracking changes of median frequency based on stationary wavelet transform. Applying to the human masseter muscle, the proposed method offers the much information for muscle fatigue, comparing with the conventional FFT-based method for muscle fatigue measurement.

• Journal of Biomedical Engineering Research
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• v.5 no.2
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• pp.133-142
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• 1984

The EMG signal can be considered as a signal source that expresses the intention of man because it is a electrical signal generated when the man contracts muscles. For proportional control of prostheses, the control signal proportional to the mousle contraction level must be estimated. Typically a foul-wave rectifier and low-pass filter are used to estimate the proportional control signal from the EMG signal. In this paper, it is proposed to use a logarithmic transformation and a linear minimum mean square error estimator. A logarithmic transformation maps the myoelectric signal into an additive control signal-plus-noise domain and the Kalman filter is used to estimate the control signal as a linear minimum mean square error estimator. The performance of this estimator is verified by the computer simulation and the estimator is applied to the EMG obtained from the biceps brachii muscle of normal subjects.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1025-1028
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• 1993

In this paper, an intelligent control scheme with multi-stage fuzzy inference is developed for a myoelectric prosthesis to achieve natural control with tactile feedback based on fuzzy control strategies. Strain gauges and a potentiometer are added to the prosthesis for tactile feedback with a PWM motor driver developed to save the battery power. According to the multi-stage fuzzy inference, the prosthesis can determine the stiffness of the object and hold an object without injuring it, meanwhile, the hysteresis phenomenon is an 80196KC single-chip microcontroller to replace the original controller.

• 전기의세계
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• v.34 no.9
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• pp.553-561
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• 1985

지금까지 EMG신호를 이용한 보철제어의 역사적 고찰과 기술적인 고려사항, 실제적인 예, 그리고 전망등에 관하여 살펴보았는데 "근육이 세계를 움직인다."는 말처럼 근전도신호는 인간-기계 상호연결에 무한한 잠재성을 보여주기 때문에 앞으로 이분야에 많은 연구가 필요하리라 생각된다. 더우기, 앞으로 몇년안에는 근육전기 제어(Myoelectric control)외에 신경으로 부터 추출된 제어신호를 이용한 신경전기제어(Neuroelectric control)도 가능하리라 믿는다. 특히, 근전도 신호처리에 관한 연구결과는 실제로 로보트제어에 기여를 하고 있는데 그 예로 Saridis의 연구결과를 들 수 있겠다. 그러므로, 근전도 신호처리에 관한 연구는 산업용 로보트 개발에도 크게 도움이 될 것이다. 최근의 첨단과학 즉, 전자공학, 컴퓨터공학, 제어공학, 반도체공학, 기계공학, 생체공학을 위시한 각 기술분야의 급격한 진보와 생리학, 체육학등 기초만 아니라 "Bionic Person" 혹은 "Artificial Man"을 가능하게 할 것이다.ificial Man"을 가능하게 할 것이다. 것이다.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.379-386
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• 1996

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) and hidden Markov models (HMM's) hybrid classifier. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or vari- ous time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrasts the multilayer feedforward networks are suitable for static patterns. Ank a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of MLP and HMM algorithms that might lead to further improved EMG recognition systems.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.265-271
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• 2018

The recent prosthetic technologies pursue to control multi-DOFs (degrees-of-freedom) hand and wrist. However, challenges such as high cost, wear-ability, and motion intent recognition for feedback control still remain for the use in daily living activities. The paper proposes a multi-channel knit band sensor to worn easily for surface EMG-based prosthetic control. The knitted electrodes were fabricated with conductive yarn, and the band except the electrodes are knitted using non-conductive yarn which has moisture wicking property. Two types of the knit bands are fabricated such as sixteen-electrodes for eight-channels and thirty-two electrodes for sixteen-channels. In order to substantiate the performance of the biopotential signal acquisition, several experiments are conducted. Signal to noise ratio (SNR) value of the knit band sensor was 18.48 dB. According to various forearm motions including hand and wrist, sixteen-channels EMG signals could be clearly distinguishable. In addition, the pattern recognition performance to control myoelectric prosthesis was verified in that overall classification accuracy of the RMS (root mean squares) filtered EMG signals (97.84%) was higher than that of the raw EMG signals (87.06%).

• Therapeutic Science for Rehabilitation
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• v.7 no.1
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• pp.79-88
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• 2018

Objective : Conventional therapy approaches for stroke survivors have required considerable demands on therapist's effort and patient's expense. Thus, new robotics rehabilitation therapy technologies have been proposed but they have suffered from less than optimal control algorithms. This article presents a novel technical healthcare solution for the real-time, simultaneous and propositional myoelectric control for stroke survivors' upper limb robotic rehabilitation therapy. Methods : To implement an appropriate computational algorithm for controlling a portable rehabilitative robot, a linear regression model was employed, and a simple game experiment was conducted to identify its potential of clinical utilization. Results : The results suggest that the proposed device and computational algorithm can be used for stroke robot rehabilitation. Conclusion : Moreover, we believe that these techniques will be used as a prominent tool in making a device or finding new therapy approaches in robot-assisted rehabilitation for stroke survivors.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.471-478
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• 2004

Electromyogram (EMC) signal generated by voluntary contraction of muscles is often used in a rehabilitation devices such as an upper limb prosthesis because of its distinct output characteristics compared to other bio-signals. This paper proposes an EMG-based human-computer interface (HCI) for the control of the above-elbow prosthesis or the wheelchair. To control such rehabilitation devices, user generates four commands by combining voluntary contraction of two different muscles such as levator scapulae muscles and flexor-extensor carpi ulnaris muscles. The muscle contraction is detected by comparing the mean absolute value of the EMG signal with a preset threshold value. However. since the time difference in muscle firing can occur when the patient tries simultaneous co-contraction of two muscles, it is difficult to determine whether the patient's intention is co-contraction. Hence, the use of the comparison method using a single threshold value is not feasible for recognizing such co-contraction motion. Here, we propose a novel method using double threshold values composed of a primary threshold and an auxiliary threshold. Using the double threshold method, the co-contraction state is easily detected, and diverse interface commands can be used for the EMG-based HCI. The experimental results with real-time EMG processing showed that the double threshold method is feasible for the EMG-based HCI to control the myoelectric prosthetic hand and the powered wheelchair.

• Journal of Oral Medicine and Pain
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• v.22 no.1
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• pp.145-155
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• 1997

The purpose of this study was to compare differences in endurance time and EMG power spectral characteristics of the masticatory muscles during sustained isometric contraction between patients and controls. 15 CMD patients{8 women and 7 men, aged 15 to 38 years(24.1$\pm$7.5)}, and 15 healthy volunteers{8 women and 7 men, aged 15 to 30 years(24.7$\pm$3.4)} without past history or present symptoms of CMD were included in this study. Sustained isometric contractions of masticatory muscles were perfomeed as long as possible at 50% level of maximum voluntary contraction(MVC) of EMG activity via visual feedback, and the duration of sustained isometric contraction(endurance time) was examined. The author perfomed EMG power spectral analysis in the myoelectric signals of masseter and anterior temporal muscle during sustained isometric contraction in CMD patients with chronic muscle pain and healthy controls. The author came to following conclusions from the results. 1. The endurance time of the patient group was shorter than the control group in sustained isometric contraction of masticatory muscles(p<0.01). 2. MF values of masticatory muscles with sustained isometric contraction during endurance time were decreased following regression line in both groups(p<0.01, r>0.9). 3. The amount of MF shift to lower frequency range exhibited no significant differences between the patients and the control group in sustained isometric contraction during endurance time. 4. SMF to lower frequency range of the patient group was steeper than the control group in sustained isometric contraction during endurance time(p<0.05).

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2356-2358
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• 2004

This thesis aims to investigate new approaches to the control strategies of human arm movements and its application for the human interface. By analyzing myoelectric signal(MES) from the arm movements of the normal human subjects, neurological informations obtained patterned could be used to identify different movement patterns of the arm movement. In this paper Artificial neural network for separation of the contraction patterns of four kinds of arm movements, i.e. and flexion and extension of the elbow and adduction and abduction of the forearm were adopted through computer simulation and experiments results were compared with the experimental added-load arm movements.