• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.364-371
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• 2014

The purpose of this paper is to review recent developments in lower limb exoskeletons. The exoskeleton system is a human-robot cooperation system that enhances the performance of the wearer in various environments while the human operator is in charge of the position control, contextual perception, and motion signal generation through the robot's artificial intelligence. This system is in the form of a mechanical structure that is combined to the exterior of a human body to improve the muscular power of the wearer. This paper is followed by an overview of the development history of exoskeleton systems and their three main applications in military/industrial field, medical/rehabilitation field and social welfare field. Besides the key technologies in exoskeleton systems, the research is presented from several viewpoints of the exoskeleton mechanism, human-robot interface and human-robot cooperation control.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3_1spc
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• pp.622-628
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• 2013

This paper describes the development of a thigh-muscle strength-assistance robot, which is a kind of wearable robot. For practicality and commercialization, we proposed three fundamental concepts: the reduction of the thigh-muscle strength, minimized degree of dependence on a powered actuator, and complete wearer safety. Based on these concepts, a spring and link bar mechanism was conceived as a novel idea. The movement of the thigh is transferred to the spring mechanism through the link bar; hence, the elastic force of the spring assists the thigh muscle. Using forse sensing resistor (FSR) sensors and a powered cam mechanism, the muscle assistance is automatically activated and deactivated according to the wearer's movement. The specific mechanisms of the robot are addressed in detail, and the effectiveness is verified by experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1034-1037
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• 2015

In recent years in the field of robotics, various methods have been developed to create an intimate relationship between people and robots. These methods include speech, vision, and biometrics recognition as well as gesture-based interaction. These recognition technologies are used in various wearable devices, smartphones and other electric devices for convenience. Among these technologies, gesture recognition is the most commonly used and appropriate technology for wearable devices. Gesture recognition can be classified as contact or noncontact gesture recognition. This paper proposes contact gesture recognition with IMU and EMG sensors by using the hidden Markov model (HMM) twice. Several simple behaviors make main gestures through the one-stage HMM. It is equal to the Hidden Markov model process, which is well known for pattern recognition. Additionally, the sequence of the main gestures, which comes from the one-stage HMM, creates some higher-order gestures through the two-stage HMM. In this way, more natural and intelligent gestures can be implemented through simple gestures. This advanced process can play a larger role in gesture recognition-based UX for many wearable and smart devices.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.53-59
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• 2022

In this paper, a mobile robot capable of remote control is designed in consideration of the user's various work environments. Specifically, a mobile remote work robot that moves in a predetermined direction and can perform a series of tasks in synchronization with the user's hand movements, and a control system and control method for controlling the robot were proposed. It was implemented using a robot hand and a wheel for movement to assist in tasks such as transporting dangerous goods or heavy goods. In order to evaluate the performance of the developed robot, the maximum weight that can be carried by the robot hand and the movable inclination of the robot were tested, and the test evaluation results satisfied most of the targeted design specifications.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.263-264
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• 2012

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.243-244
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1463-1464
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• 2011

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.295-296
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• 2011

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.8
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• pp.771-782
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• 2017

This paper describes the development of a wearable robot to assist ankle power for the elderly. Previously developed wearable robots have generally used motors and gears to assist muscle power during walking. However, the combination of motor and reduction gear is heavy and has limitations on the simultaneous control of stiffness and torque due to the friction of the gear reducer unlike human muscles. Therefore, in this study, Mckibben pneumatic muscle, which is lighter, safer, and more powerful than an electric motor with gear, was used to assist ankle joint. Antagonistic actuation using a pair of pneumatic muscles assisted the power of the soleus muscles and tibialis anterior muscles used for the pitching motion of the ankle joint, and the model parameters of the antagonistic actuator were experimentally derived using a muscle test platform. To recognize the wearer's walking intention, foot load and ankle torque were calculated by measuring the pressure and the center of pressure of the foot using force and linear displacement sensors, and the stiffness and the torque of the pneumatic muscle joint were then controlled by the calculated ankle torque and foot load. Finally, the performance of the developed ankle power assistive robot was experimentally verified by measuring EMG signals during walking experiments on a treadmill.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1751-1768
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• 2011

Falls are one of the most concerned accidents for elderly people and often result in serious physical and psychological consequences. Many researchers have studied fall detection techniques in various domain, however none released to a commercial product satisfying user requirements. We present a systematic modeling and evaluating procedure for best classification performance and then do experiments for comparing the performance of six procedures to get a statistical classifier based wrist-type fall detector to prevent dangerous consequences from falls. Even though the wrist may be the most difficult measurement location on the body to discern a fall event, the proposed feature deduction process and fall classification procedures shows positive results by using data sets of fall and general activity as two classes.