• 한국전문물리치료학회지
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• 제18권2호
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• pp.27-34
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• 2011

The aim of this study was to determine the effect of action-observation training on arm function in people with stroke. Fourteen chronic stroke patients participated in action-observation training. Initially, they were asked to watch video that illustrated arm actions used in daily activities; this was followed by repetitive practice of the observed actions for 3 times a week for 3 weeks. Each training session lasted 30 min. All subject participated 12 training session on 9 consecutive training days. For the evaluation of the clinical status of standard functional scales, Wolf motor function test was carried out at before and after the training and at 2 weeks after the training. Friedman test and Wilcoxon signed rank test was used to analyze the results of the clinical test. There was a significant improvement in the upper arm functions after the 3-week action-observation training, as compared to that before training. The improvement was sustained even at two weeks after the training. This result suggest that action observation training has a positive additional impact on recovery of stroke-induced motor dysfunctions through the action observation-action execution matching system, which includes in the mirror neuron system.

• 제어로봇시스템학회논문지
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• 제20권6호
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• pp.606-611
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• 2014

The understanding of another's behavior is a fundamental cognitive ability for primates including humans. Recent neuro-physiological studies suggested that there is a direct matching algorithm from visual observation onto an individual's own motor repertories for interpreting cognitive ability. The mirror neurons are known as core regions and are handled as a functionality of intent recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper, we addressed previous works used to model the function and mechanisms of mirror neurons and proposed a computational model of a mirror neuron system which can be used in human-robot interaction environments. The major focus of the computation model is the reproduction of an individual's motor repertory with different embodiments. The model's aim is the design of a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and execution. We also propose a biologically inspired plausible equation model.

• PNF and Movement
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• 제12권2호
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• pp.123-132
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• 2014

Purpose: The discovery of mirror neuron system may positively affect functional recovery; therefore, rehabilitation is needed that is practical for use in clinical settings. The purpose of this study was to examine the effect of action observation training on upper motor function in people who had suffered strokes. Methods: Three elderly patients with stroke, aged to years, were recruited from a stroke rehabilitation center. A nonconcurrent, multiple baseline subject approach was taken, with an A-B-A treatment single-subject experimental design, and the experiment was conducted for 3 weeks. The action observation training was repeated 5 times in 5 days during the intervention period. The arm function, including WMFT, BBT, and grip and pinch strength, was evaluated in each subject 5 times during the baseline period, the intervention period, and the baseline regression period. Results: The results of the evaluation for each subject were presented as mean values and video graphs. The WMFT scores of 2 subjects were improved during the intervention period in comparison with the baseline period, and this improvement was maintained even during the regression baseline period. The BBT and the grip and pinch strength were not improved. Conclusion: Based on these results, we suggest that the action observation training for 5 sessions was effective in improving upper limb function of stroke patients but was not effective in improving hand dexterity or grip and pinch strength.

• 대한임베디드공학회논문지
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• 제13권1호
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• pp.17-24
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• 2018

Military camera equipment has a problem that observability is inferior due to various shaking factors. In this paper, we propose an image stabilization algorithm considering performance and execution time to solve this problem and implemented it in Zynq SoC. We stabilized both the simple shaking in the fixed observation position and the sudden shaking in the moving observation position. The feature of the input image is extracted by the Sobel edge algorithm, the subblock with the large edge data is selected, and the motion vector, which is the compensation reference, is calculated through template matching using the 3-step search algorithm of the region of interest. In addition, the proposed algorithm can distinguish the shaking caused by the simple shaking and the movement by using the Kalman filter, and the stabilized image can be obtained by minimizing the loss of image information. To demonstrate the effectiveness of the proposed algorithm, experiments on various images were performed. In comparison, PSNR is improved in the range of 2.6725~3.1629 (dB) and image loss is reduced from 41% to 15%. On the other hand, we implemented the hardware-software integrated design using HLS of Xilinx SDSoC tool and confirmed that it operates at 32 fps on the Zynq board, and realized SoC that operates with real-time processing.