• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1231-1239
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• 2015

We developed a robot that has four limbs, each of which has the same kinematic structure and has 6 degrees of freedom. The robot is 600mm high and weighs 4.3kg. The robot can perform walking and manipulating task by using the four limbs selectively. The robot has three walking patterns. The first one is biped walking, which uses two rear limbs as legs and two front limbs as arms. The second one is biped walking with supporting arms, which is basically biped walking but uses two arms as supporting legs for increasing stability of the robot. The last one is quadruped walking, which uses all the four limbs as legs. When a task for the robot is given, the robot approaches the task point by selecting an appropriate walking pattern among three walking patterns and performs the task. The robot has many degrees of freedom and is a redundant system for a three dimensional task. We propose a redundancy resolution method, in which the robot’s translational move to the task point is modeled as a prismatic joint and optimal solutions are obtained by optimizing some performance criteria. Several simulations are performed for the validity of the proposed method.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.318-325
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• 2019

NREX, an upper limb exoskeleton robot, was developed at the National Rehabilitation Center to assist in the upper limb movements of subjects with weak muscular strength and control ability of the upper limbs, such as those with hemiplegia. For the free movement of the shoulder of the existing NREX, three passive joints were added, which improved its wearability. For the flexion/extension movement and internal/external rotation movement of the shoulder of the robot, the ball lock pin is used to fix or rotate the passive joint. The force and torque between a human and a robot were measured and analyzed in a reaching movement for four targets using a six-axis force/torque sensor for 20 able-bodied subjects. The addition of two passive joints to allow the user to rotate the shoulder can confirm that the average force of the upper limb must be 31.6% less and the torque must be 48.9% less to perform the movement related to the axis of rotation.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.87-90
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• 2001

This paper presents a design of new type biped robot and dynamic walking simulation for this system. The robot is distinguished from other one by which has a parallel mechanism type trunk and lead-screw type actuators to drive the joints of the trunk, knee and ankle. The basic consideration on the design is that it is able to accommodate itself to human's daily environments without any other modification of around and also to operate its upper limbs more smoothly with a spine functional trunk. It is designed according to a human with about 130 cm height and about 30 kg weight. And it also is able to dynamically walk on an even ground. It has constructed with total 14 DOFs which have two legs, a hip, and a trunk. The joints of each leg and trunk are adopted with a parallel structure which has good kinematic characteristics and take light weight. To test of the capacity of joint actuators and to analysis of the dynamic properties of the biped robot, optimized trunk trajectory is determined by means of an approximated FFT method based on ZMP criteria, and dynamic simulation is performed using DADS with a 1.1 time/step velocity on the even ground during four steps.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.459-466
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• 2016

This study examined the effects of robot-assisted therapy on the motor and functional recovery of the lower limbs in 53 subacute stroke patients. Robot-assisted therapy was performed using Lokomat? (Hocoma AG, Zurich, Switzerland) for thirty minutes per day, five times a week for four weeks. The outcome measures used were the Fugl-Meyer assessment, Motricity index(MI), Functional ambulation category(FAC), Berg balance scale(BBS) for gait function and balance ability, 10m walking test, K-Modified Barthel Index(K-MBI) for the activities of daily living and Mini mental state examination (MMSE), and Beck's depression inventory(BDI) for depression. All patients recruited underwent these evaluations before and after the four week robot-assisted therapy. For the evaluation, the somatosensory evoked potentials were used to assess the functional recovery. Robot-assisted therapy on the lower limb after subacute stroke showed improvement in motor strength, gait function, and the activities of daily living. All changes in terms of MI, FAC, BBS, and K-MBI exhibited a statistically significant difference after the four weeks robot-assisted therapy. The somatosensory evoked potential result showed a correlation with the MI and K-MBI. Robot-assisted therapy is believed to facilitate the motor and functional recovery of the lower limb in subacute stroke patients.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.59-64
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• 2015

In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.