• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.73-74
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• 2009

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.927-932
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• 2010

Estimation methods of motion intention from bio-signal present challenges in man machine interaction(MMI) to offer user's command to machine without control of any devices. Measurements of meaningful bio-signals that contain the motion intention and motion estimation methods from bio-signal are important issues for accurate and safe interaction. This paper proposes a novel motion estimation sensor based on a geometrical muscle changes, and a motion estimation method using the sensor. For estimation of the motion, we measure the circumference change of the muscle which is proportional to muscle activation level using a flexible piezoelectric cable (pMAS, piezo muscle activation sensor), designed in band type. The pMAS measures variations of the cable band that originate from circumference changes of muscle bundles. Moreover, we estimate the elbow motion by applying the sensor to upper limb with least square method. The proposed sensor and prediction method are simple to use so that they can be used to motion prediction device and methods in rehabilitation and sports fields.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.114-120
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• 2018

In this paper we implemented an EMG sensor system for wearable devices to obtain and analyze of EMG signals. The performance of the implemented sensor system is evaluated by the correlation analysis of muscle fatigue and muscle activation to clinical EMG system and compared with power consumption of the measured power of our system and commercial systems. In experiments with biceps and triceps brachii of 5 objects, The correlation values of muscle fatigue and muscle activation between our system and the clinical EMG system is 1.1~1.4 and about 1.0, respectively. And also the power consumption of our system is 25~50% less than that of some commercial EMG sensor systems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.1029-1030
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• 2010

• Journal of the Korean Society for Precision Engineering
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• v.30 no.6
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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.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.7
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• pp.15-21
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.985-986
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• 2013

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1169-1172
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• 2005

For elderly people, an advanced training machine that uses actuator and can adjust load according to muscle activity is proposed. The proposed machine allows users to have a safe and effective training through exercise close to ordinal motion appears in daily life such as stretching or stooping motion. A muscle activity sensor real-timely monitors the activation level of user's muscle during the exercise and the training load is adjusted based on the measured data. The training load is exerted and continuously controlled by electric/pneumatic actuator.

• Journal of International Academy of Physical Therapy Research
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• v.12 no.2
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• pp.2308-2313
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• 2021

Background: Although the scapular posterior tilt movement could facilitate the lower trapezius (LT) muscle activity, no study identified the effects of the scapular posterior tilt movement on the selective activation of the LT muscle during prone shoulder extension. Objectives: To examine the influences of additional scapular posterior tilt on electromyography (EMG) of the upper trapezius (UT) and the LT muscles during prone shoulder extension. Design: Cross-sectional study. Methods: There were 15 asymptomatic male participants in this study who performed prone shoulder extension with and without scapular posterior tilt movements. For the scapular posterior tilt movements, participants performed visual biofeedback training for scapular movement using motion sensor. During the exercises, the EMG activity of the UT and LT was recorded using surface EMG system. Results: The EMG activity of the LT significantly increased during prone shoulder extension with scapular posterior tilt compared to that of general prone shoulder extension, whereas that of the UT was not significantly different between the two exercises. Moreover, scapular posterior tilt application significantly decreased UT/LT muscle activity ratio. Conclusion: Scapular posterior tilt movement may be emphasized during exercise when facilitating LT muscle activation.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.8
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• pp.870-877
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• 2013

There has been much recent research interest in developing numerous kinds of human-machine interface. This field currently requires more accurate and reliable sensing systems to detect the intended human motion. Most conventional human-machine interface use electromyography (EMG) sensors to detect the intended motion. However, EMG sensors have a number of disadvantages and, as a consequence, the human-machine interface is difficult to use. This study describes a muscle volume sensor (MVS) that has been developed to measure variation in the outline of a muscle, for use as a human-machine interface. We developed an algorithm to calibrate the system, and the feasibility of using MVS for detecting muscular activity was demonstrated experimentally. We evaluated the performance of the MVS via isotonic contraction using the KIN-COM$^{(R)}$ equipment at torques of 5, 10, and 15 Nm.