• Fashion & Textile Research Journal
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• v.24 no.3
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• pp.333-345
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• 2022

This study developed and evaluated the motion control of 3D printed fingers applied to smart gloves. Four motions were programmed by assembling the module using the Arduino program: cylindrical grasping, spherical grasping, tip-to-tip pinch gripping, and three-jaw pinch gripping. Cap and re-entrant (RE) strip types were designed to model the finger. Two types of modeling were printed using filaments of thermoplastic elastomer (TPE) and thermoplastic polyurethane (TPU). The prepared samples were evaluated using three types of pens for cylidrical grasping, three types of balls for spherical grasping, and two types of cards for tip-to-tip pinch gripping and three-jaw pinch gripping. The motion control of fingers was connected using five servo motors to the number of each control board. Cylindrical and spherical grasping were moved by controlling the fingers at 180° and 150°, respectively. Pinch gripping was controlled using a tip-to-tip pinch motion controlled by the thumb at 30° and index-middle at 0° besides a three-jaw pinch motion controlled by the thumb-index finger-middle at 30°, 0°, and 0°, respectively. As a result of the functional evaluation, the TPE of 3D-printed fingers was more flexible than those of TPU. RE strip type of 3D-printed fingers was more suitable for the motion control of fingers than the 3D-printed finger.

• Journal of Acupuncture Research
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• v.32 no.3
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• pp.53-60
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• 2015

Purpose : This study aimed to prove that surface electromyography(SEMG) can be used to identify the degree of symptoms of diseases in the upper extremities; it also aimed to confirm various potential therapeutic effects through an inquiry into the value measured by the SEMG on certain acupuncture points in the upper extremities. Methods : Fifty healthy people received instructions for the method of exercise: wrist flexion, extension and hand grasping. Disposable electrodes were attached to acupuncture points of Susamni($LI_{10}$), Naegwan($PC_6$), Oegwan($TE_5$) and below Sohae($HT_3$) two cun on both sides in flexion, extension and grasping to measure the SEMG values. The research results were analyzed using SPSS statistics Ver. 22.0(IBM, USA). Results : The average value was highest on Naegwan($PC_6$) in grasping, and the average SEMG value was higher in the order of grasping, extension and flexion. The asymmetry index of each point was Susamni($LI_{10}$) $16.26{\pm}13.59%$, Oegwan($TE_5$) $20.38{\pm}15.59%$, below Sohae($HT_3$) two cun $20.89{\pm}16.77%$, Naegwan($PC_6$) $22.49{\pm}14.91%$ in wrist extension, Susamni($LI_{10}$) $25.99{\pm}21.44%$, Oegwan($TE_5$) $21.15{\pm}15.94%$, below Sohae($HT_3$) two cun $19.62{\pm}15.46%$, Naegwan($PC_6$) $19.93{\pm}16.85%$ in wrist flexion, Susamni($LI_{10}$) $16.60{\pm}12.21%$, Oegwan($TE_5$) $10.94{\pm}8.29%$, below Sohae($HT_3$) two cun $15.20{\pm}12.60%$, Naegwan($PC_6$) $11.68{\pm}7.77%$ in grasping. Conclusions : In this study, to identify the degree of symptoms of diseases in the upper extremities and confirm therapeutic effects, it is necessary to analyze the calculated percentage and compare the SEMG measurement of special points with those of other points, and with the asymmetry index.

• The Korean Journal of Rehabilitation Nursing
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• v.3 no.2
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• pp.196-211
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• 2000

The purpose of this study was to determine the effects of 5 weeks self-help management program on reducing depression, promoting Activity of Daily Livings(ADLs), Instrumental Activity of Daily Livings(IADLs), grasping power, hope and self-efficacy for post stroke patients visiting public health care centers in Seoul. This was pre-experimental study, and the subjects were 27 post stroke hemiplegic patients. The program was composed of five sessions and each session had health education on stroke, ROM exercise and recreation. Data were collected from May to November, 2000, and all subjects were asked to complete the Questionnaires, be measured vital signs and grasping power. Data were analyzed with frequency, percent, paired t-test, and Pearson's correlation coefficient using SAS(version 6.12) program. The results were as follows : 1) The scores of ADLs were increased from 27.04 to 28.22 after program, and that was statistically significant(p=.005). 2) The scores of IADLs were increased from 18.70 to 19.78 after program, and that was statistically significant(p=.004). 3) The grasping power of right hand were increased from 21.87kg to 26.93kg after program, and that was statistically significant(p=.002). But the grasping power of left hand were statistically insignificant(p=.919). 4) The scores of depression were decreased from 39.63 to 35.30 after program, and that was statistically significant(p=.030). 5) The scores of hope were increased from 30.89 to 34.15 after program, and that was statistically significant(p=.002). 6) The scores of self-efficacy were increased from 67.70 to 76.37 after program, and that was statistically significant(p=.000). According to the results of this study, the scores of Activity of Daily Livings(ADLs), Instrumental Activity of Daily Livings (IADLs), hope, and self-efficacy and the grasping power were improved and depression was reduced in post stroke patients participating in self-help management program. Therefore we recommend to use self-help management programs as a nursing intervention for the post stroke patient.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.363-370
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• 2022

Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.11
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• pp.37-45
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• 2010

Stroke patients can't use their hands because of the paralysis of their fingers. Their fingers are recovered by rehabilitating training, and the rehabilitating extent can be judged by grasping a spherical object. At present, the used object in hospital is only a spherical object, and can't measure the force of fingers. Therefore, doctors judge the rehabilitating extent by touching and watching at their fingers. So, the spherical object measuring system which can measure the force of their fingers should be developed. In this paper, the finger-force measuring system with a six-axis force/moment sensor which can measure the spherical-object grasping force is developed. The six-axis force/moment sensor was designed and fabricated, and the force measuring device was designed and manufactured using DSP (digital signal processing). Also, the grasping force test of men was performed using the developed finger-force measuring system, it was confirmed that the average force of men was about 120N.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.197-204
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• 2020

In this paper, we present a learning platform for robotic grasping in real world, in which actor-critic deep reinforcement learning is employed to directly learn the grasping skill from raw image pixels and rarely observed rewards. This is a challenging task because existing algorithms based on deep reinforcement learning require an extensive number of training data or massive computational cost so that they cannot be affordable in real world settings. To address this problems, the proposed learning platform basically consists of two training phases; a learning phase in simulator and subsequent learning in real world. Here, main processing blocks in the platform are extraction of latent vector based on state representation learning and disentanglement of a raw image, generation of adapted synthetic image using generative adversarial networks, and object detection and arm segmentation for the disentanglement. We demonstrate the effectiveness of this approach in a real environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.22-27
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• 2012

This paper presents a ball screw-driven robotic gripper mechanism which is possible to grasp an object and analyzes its kinematic feature for grasping by simulation. For the purpose of identifying the feature of the robot gripper, we try to confirm the kinematics relating the joint space of the driving actuator to the gripper's tip space. To be specific, the proposed robot gripper employs one actuator and a symmetrical closed-chain structure. As a result, the specified robot gripper has an advantage of robustness to external forces structurally, and it is easy to implement simple grasping operations. Also the gripper has a useful squeezing effect for power grasping.

• Journal of muscle and joint health
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• v.23 no.3
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• pp.169-178
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• 2016

Purpose: The purpose of this study was to evaluate effects of a cognitive improvement program on cognition, activities of daily living (ADL), depression, life satisfaction, and grasping power in small groups. Methods: A nonequivalent control group pretest-posttest design was used. Participants were 127 elders who were never diagnosed with dementia and could read (experimental: 61, control: 66). The program was consisted of 8 sessions to improve vocabulary and writing skills and fine motor skills. Each session was applied once a week for 8 weeks. A group was consisted of less than five members. Results: Cognition (t=3.82, p<.001), depression (t=-2.24, p=.027), life satisfaction (t=2.25, p=.027), and grasping power (Rt: t=2.44, p=.016, Lt: t=2.63, p=.010) except ADL (t=-0.49, p=.622) were significantly increased in the experimental group after the cognitive improvement program compared to the control group. Conclusion: This study indicates that the cognitive improvement program was effective to improve cognition, depression, life satisfaction and grasping power. Further study is needed in order to identify continuous effects of this cognitive improvement program.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.1
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• pp.89-96
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• 2009

This paper describes the intelligent robot's hand with three finger sensors for a humanoid robot. In order to grasp an unknown object safely, the intelligent robot's hand should measure the mass of the object, and determine the grasping force using the mass, finally control the grasping force using the finger sensors and the controller. In this paper, the intelligent robot's hand for a humanoid robot was developed. First, the six-axis force/moment sensor was manufactured. second, three finger force sensors were designed and fabricated, third, the high-speed controller was manufactured using DSP(digital signal processor), finally, the characteristic test for determining a grasping force and for grasping an unknown object safely It is confirmed that the hand could grasp an unknown object safely.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.415-420
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• 2017

Underactuated prosthetic hands are relatively light and economical. In this work, an economical grasping force control system is proposed for underactuated prosthetic hands with adaptive grasp capability. The prosthetic hand is driven by a main cable based on a set of electromyography sensors on the forearm of a user. Part of the main cable tension related to grasping force is fed back to the user by a skin-mounted vibrator. The proper relationship between the grasping force and the vibrator drive voltage was established and prototype tests were performed on a group of users. Relatively accurate grasping force control was achieved with minimal training of users.