• 대한의용생체공학회:의공학회지
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• 제28권1호
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• pp.29-35
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• 2007

This study was designed to investigate the feasibility to utilize the electromyogram (EMG) for estimating the muscle torque. The muscle torque estimation plays an important role in functional electrical stimulation because electrical stimulation causes muscles to fatigue much faster than voluntary contraction, and the stimulation intensity should then be modified to keep the muscle torque within the desired range. We employed the neural network method which was trained using the major EMG parameters and the corresponding knee extensor torque measured and extracted during isometric contractions. The experimental results suggested that (1) our neural network algorithm and protocol was feasible to be adopted in a real-time feedback control of the stimulation intensity, (2) the training data needed to cover the entire range of the measured value, (3) different amplitudes and frequencies made little difference to the estimation quality, and (4) a single input to the neural network led to a better estimation rather than a combination of two or three. Since this study was done under a limited contraction condition, the results need more experiments under many different contraction conditions, such as during walking, for justification.

• 한국정밀공학회지
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• 제30권6호
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• pp.650-657
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• 2013

This study uses a muscle activation sensor and elbow joint model to develop an estimation algorithm for human elbow joint torque for use in a human-robot interface. A modular-type MVS (Muscle Volume Sensor) and calibration algorithm are developed to measure the muscle activation signal, which is represented through the normalization of the calibrated signal of the MVS. A Hill-type model is applied to the muscle activation signal and the kinematic model of the muscle can be used to estimate the joint torques. Experiments were performed to evaluate the performance of the proposed algorithm by isotonic contraction motion using the KIN-COM$^{(R)}$ equipment at 5, 10, and 15Nm. The algorithm and its feasibility for use as a human-robot interface are verified by comparing the joint load condition and the torque estimated by the algorithm.

• 한국정밀공학회지
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• 제28권6호
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• pp.670-677
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• 2011

This study is to estimate the joint torques without torque sensor using the EMG (Electromyogram) signal of agonist/antagonist muscle with Neural Network Back Propagation Algorithm during the elbow motion. Command Signal can be guessed by EMG signal. But it cannot calculate the joint torque. There are many kinds of field utilizing Back Propagation Learning Method. It is generally used as a virtual sensor estimated physical information in the system functioning through the sensor. In this study applied the algorithm to obtain the virtual senor values estimated joint torque. During various elbow movement (Biceps isometric contraction, Biceps/Triceps Concentric Contraction (isotonic), Biceps/Triceps Concentric Contraction/Eccentric Contraction (isokinetic)), exact joint torque was measured by KINCOM equipment. It is input to the (BP)algorithm with EMG signal simultaneously and have trained in a variety of situations. As a result, Only using the EMG sensor, this study distinguished a variety of elbow motion and verified a virtual torque value which is approximately(about 90%) the same as joint torque measured by KINCOM equipment.

• 대한의용생체공학회:의공학회지
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• 제22권3호
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.

• 한국전문물리치료학회지
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• 제29권4호
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• pp.262-268
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• 2022

Background: Virtual reality (VR) programs based on motion capture camera are the most convenient and cost-effective approaches for remote rehabilitation. Assessment of physical function is critical for providing optimal VR rehabilitation training; however, direct muscle strength measurement using camera-based kinematic data is impracticable. Therefore, it is necessary to develop a method to indirectly estimate the muscle strength of users from the value obtained using a motion capture camera. Objects: The purpose of this study was to determine whether the pedaling speed converted using the VR engine from the captured foot position data in the VR environment can be used as an indirect way to evaluate knee muscle strength, and to investigate the validity and reliability of a camera-based VR program. Methods: Thirty healthy adults were included in this study. Each subject performed a 15-second maximum pedaling test in the VR and built-in speedometer modes. In the VR speedometer mode, a motion capture camera was used to detect the position of the ankle joints and automatically calculate the pedaling speed. An isokinetic dynamometer was used to assess the isometric and isokinetic peak torques of knee flexion and extension. Results: The pedaling speeds in VR and built-in speedometer modes revealed a significantly high positive correlation (r = 0.922). In addition, the intra-rater reliability of the pedaling speed in the VR speedometer mode was good (ICC [intraclass correlation coefficient] = 0.685). The results of the Pearson correlation analysis revealed a significant moderate positive correlation between the pedaling speed of the VR speedometer and the peak torque of knee isokinetic flexion (r = 0.639) and extension (r = 0.598). Conclusion: This study suggests the potential benefits of measuring the maximum pedaling speed using 3D depth camera in a VR environment as an indirect assessment of muscle strength. However, technological improvements must be followed to obtain more accurate estimation of muscle strength from the VR cycling test.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.85.3-85
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• 2002

In this study, we tried to investigate the impedance characteristic of human arm in a cooperative task. Human arm was moved in a desired trajectory. The motion was actuated by a 1 degree-of-freedom robot system. As the muscle is mechanically analogous to a spring-damper system, a second-order equation was considered as the model for arm dynamics. In the model, inertia, stiffness and damping factor were considered. The impedance parameter was estimated from the position and torque data obtained from the experiment and based on the "Estimation of Parametric Model". It was found that the inertia is almost constant over the operational time. The damping factor and stiffness were high...

• 한국산학기술학회논문지
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• 제18권10호
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• pp.442-447
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• 2017

이 연구의 목적은 등속성 장비를 이용하여 고등학교 야구선수들을 대상으로 주관절 근육의 평가 기준치를 설정하는데 있다. 상지에 의학적으로 문제가 없는 고등학교 야구선수 201명을 대상으로 하였다. 주관절 검사는 HUMAC NORM(CSMI, USA) system을 이용하여 굴곡, 신전운동을 부하속도 $60^{\circ}/sec$에서 4회를 실시하였으며, 절대근력(peak torque)와 상대근력(peak torque % body weight)을 측정하였다. Cajori의 5단계(6.06%, 24.17%, 38.30%, 24.17%, and 6.06%)로 구분한 구간추정(interval estimation)방식에 의해 평가 기준치를 나타내었다. 이 연구의 결과에서 각속도 $60^{\circ}/sec$에서 주관절의 절대근력(peak torque) 굴근의 평균은 $37.88{\pm}8.14Nm$ 신근은 $44.59{\pm}11.79Nm$으로 나타났으며, 상대근력(peak torque % body weight) 굴근의 평균은 $50.06{\pm}8.66Nm$ 신근은 $58.28{\pm}12.84Nm$로 나타났다. 각속도 $60^{\circ}/sec$에서 주관절 굴근(flexer)과 신근(extensor)의 절대근력(peak torque)과 상대근력(peak torque % body weight)의 평가기준치는 설정하였다. 이 연구에서 분석 된 결과를 토대로 다음과 같이 결론을 이끌어 냈다. 주관절은 야구선수들에게 가장 많이 발생되는 손상임에도 불구하고 국내외 주관절 근력에 관한 연구가 미비한 실정이다. 주관절 손상을 예방하고 경기력 향상을 위해 상대적 근력 수준과 훈련효과를 보다 객관적으로 평가할 수 있는 근력 척도가 필요하다. 주관절의 절대근력과 상대근력의 평가기준치를 제시함으로서 고교선수, 야구지도자, 선수트레이너, 재활전문가 등에게 손상회복 및 재활 복귀 시 유용하게 자료가 될 것으로 생각되며, 객관적인 임상 평가 데이터로 활용하는 데 매우 유용한 정보라고 사료된다.