• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.824-832
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• 2011

An essential consideration to differentiate prosthetic hand from robot hand is its convenience and usefulness rather than high resolution or multi-function of the robot hand. Therefore, this study proposes a myoelectric hand with a 2 DOF auto wrist module which has 6 essential functions of the human hand such as open, grasp, pronation, supination, extension, flexion, which improves the convenience of the daily life. It consists of the 3 main parts, the myoelectric sensor for input signal without additional attachment to operate the prosthetic hand, hand mechanism with high-torqued auto-transmission mechanism and self-locking module which guarantee the safety under the abrupt emergency and minimum power consumption, and dual threshold based controller to make easy for adopting the multi-DOF myoelectric hand. We prove the validity of the proposed system with experimental results.

• Journal of Biomedical Engineering Research
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• v.9 no.2
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• pp.211-214
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• 1988

As a muscular contraction is sustained, the power spectrum of the myoelectric signal is compressed into lower frequencies. The median frequency appears to be the prefered parameters to monitor this compression. This paper describes a technique and a device which provide an estimate of the median frequency using a TMS32020 DSP chip and IBM PC for tracking of this parameter. Results obtained from myoelectric signal are presented and discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.714-717
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• 1988

As a muscular contraction is sustained, the power spectrum of the myoelectric signal is compressed into lower frequencies. The median frequency appears to be the prefered parameters to monitor this compression. This paper describes a technique and a device which provide an estimate of the median frequency using a TMS32020 DSP chip and IBM PC for tracking of this parameter. Results obtained from myoelectric signal are presented and discussed.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.11
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• pp.36-42
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• 2000

• 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.20 no.3
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• pp.307-314
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• 1999

This paper describes an informatlon compression of electrically evoked myoelectric signal, M-wave. This wave shows a direct response m lato-response of nerve conductlQn study and has a characteristic with finite time support. M-wave is a useful factor for investing neurodi~ease and is often desirable to have a compact description of its shape and time evolution. The aim of this paper is to show that the AR modeling IS a effective method for compressing an information of M-wave. First, AR model parameters of real M-wave are estimated. And then. they are verified by approximatmg a M-wave using estimated AR parameters and by comparing to other melhod, Hermite tlansform[4]. To concretely evaluate the proposed method, the NMSE(normalized mean square error) of approximation curves are compared. As a result, AR modeling is effective for M-wave assessment because of its capability for the information compression.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.393-399
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• 2023

Upper limb amputees wear an upper limb prosthesis for both aesthetic purposes and functional necessity, and in particular, in the case of amputee with both hands, it is essential to wear a myoelectric prosthetic hand capable of gripping action. The prosthetic hand operated by the EMG signal of the remaining muscles is a public insurance benefit item of the Industrial Accident Compensation Insurance, and test method standards are needed to be developed for the safety of the user and the effectiveness of the product performance. In this study, we developed systems for measuring the gripping force of myoelectric hand prosthesis by a load cell and for durability test of the prosthesis over repeated use with a proximity sensor, and propose a test method standard. Since the international test method standard has not yet been established, it is expected that Korea will be able to play a leading role in this standardization field in the future.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1417-1418
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• 2013

• Journal of Biomedical Engineering Research
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• v.14 no.2
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• pp.113-123
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• 1993

The aim of this study is to design a electrogoniometer for the measurement of three dimensional human elbow joint rotations. Using this device and visual monitor, we measured the angle of elbow joint rotations during the goal-directed movements. And we extracted myoelectric signals(MES) to verify the inter-relationship of elbow joint activities and constructed a system for the analysis of the spectrum for MES. The data obtained from this system will be used for the controller signal of prosthetic arm.

• 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.