• Journal of Sensor Science and Technology
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• v.25 no.1
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• pp.27-34
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• 2016

This paper describes the development of a rehabilitation robot that can provide wrist bending exercise to a severe stroke patient staying in a bed ward or at home. The developed rehabilitation robot has a three-axis force sensor which detects three directional force Fx, Fy, and Fz. The sensor measures a bending force (Fz) exerted on the wrist and the signal force (Fx and Fy) which can be used for the safety purpose. The robot was designed for severe stroke patients in bed, and the robot program was developed to perform a wrist bending rehabilitation exercise. In our tests including a nine-day experimental exercise, the developed force sensor-based robot operated effectively and safely.

• Journal of Sensor Science and Technology
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• v.22 no.2
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• pp.118-123
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• 2013

Most serious stroke patients have the paralysis of their wrists, and can't use of their hands freely. But their wrists can be recovered by rehabilitation exercise. Recently, professional rehabilitation therapeutists exercise the wrists of stroke patients in hospital. But the wrists of stroke patients have not rehabilitated, because the therapeutists are much less than stroke patients in number. Therefore, the wrist bending-exercise rehabilitation robot that can measure the bending force of the patients' wrists is developed. In this paper, the three-axis force sensor was designed for the wrist bending-exercise rehabilitation robot. As a test results, the interference error of the three-axis force sensor was less than 0.85%. It is thought that the sensor can be used to measure the wrist bending force of the patient.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1011-1016
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• 2013

Patients have the paralysis of their wrists, and can't use of their wrists freely. But their wrists can be recovered by wrist-bending rehabilitation exercise. Professional rehabilitation therapeutists exercise the wrists of patients in hospital. But the wrists of patients have not exercised enough for the rehabilitation, because the therapeutists are much less than patients in number. Therefore, the wrist rehabilitation robot should be developed, and it have to measure the applied force to the patients' wrists for their safety. In this paper, the four-axis force/moment sensor was designed for the wrist rehabilitation robot. As a test results, the interference error of the four-axis force/moment sensor was less than 0.91%. It is thought that the sensor can be used to measure the applied force to the patients' wrists.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.5
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• pp.529-536
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• 2013

Most serious stroke patients have the paralysis on their wrists, and can't use their hands freely. But their wrists can be recovered by rehabilitation exercises. Recently, professional rehabilitation therapeutists help stroke patients exercise their wrists in hospital. But it is difficult for them to rehabilitate their wrists, because the therapeutists are much less than stroke patients in number. Therefore, the wrist twist-exercise rehabilitation robot that can measure the twist force of the patients' wrists is needed and developed. In this paper, the six-axis force/moment sensor was designed appropriately for the robot. As a test result, the interference error of the six-axis force/moment sensor was less than 0.85%. It is thought that the sensor can be used to measure the wrist twist force of the patient.

• Journal of Biomedical Engineering Research
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• v.40 no.3
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• pp.116-124
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• 2019

The rehabilitation training robots for treating the upper limbs of stroke patients were mainly focused on the upper proximal treatment of it, but recently studies of the distal parts of the upper limbs for rehabilitation of the hand is making some progress even though it is still a small number so far. In this paper, we present the hand robot for the rehabilitation training of stroke patients that is the fingertip contact-typed mechanism, and it has also equipped with the wrist rehabilitation unit to be worked like human hand that enables any movements through mutual cooperation by fingers while picking up or grasping objects. The robot that is presented for this purpose supports the movement of fingers with 5-DoF and the wrist with 3-DoF that moves independently, and operates with a structure that allows the joints of the wrist and fingers to be collaborated organically together to each other. Also, hereby the simulation and evaluation test on its robot mechanism are performed to ensure that fingers with 5-DoF and the wrist with 3-DoF of the serial kinematical mechanism are designed to comply with or exceed ROM for ADL.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1025-1026
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• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1021-1022
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• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1023-1024
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• 2013

• 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 rehabilitation welfare engineering & assistive technology
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• v.2 no.1
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• pp.45-49
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• 2009

This paper proposes a data glove for a rehabilitation robot interface for the upper extremity paralysis. The designed data glove uses seven flexible sensors so as to measure the flexion angles of fingers and wrist. We verified the performance of the data glove using a 3D graphic interface developed. The experimental results show that the proposed data glove is feasible to sense hand motions and applicable to the robot interface.