• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7508-7512
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• 2015

We propose a brain-machine interface(BMI) based human-robot interaction(HRI) platform which operates machines by interfacing intentions by capturing brain waves. Platform consists of capture, processing/mapping, and action parts. A noninvasive brain wave sensor, PC, and robot-avatar/LED/motor are selected as capture, processing/mapping, and action part(s), respectively. Various investigations to ensure the relations between intentions and brainwave sensing have been explored. Case studies-an interactive game, on-off controls of LED(s), and motor control(s) are presented to show the design and implementation process of new BMI based HRI platform.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.212-220
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• 2013

Recently, there has been tremendous increase in human-computer/machine interaction system, where the goal is to provide with an appropriate service to the user at the right time with minimal human inputs for human augmented cognition system. To develop an efficient human augmented cognition system based on human computer/machine interaction, it is important to interpret the user's implicit intention, which is vague, in addition to the explicit intention. According to cognitive visual-motor theory, human eye movements and pupillary responses are rich sources of information about human intention and behavior. In this paper, we propose a novel approach for the identification of human implicit visual search intention based on eye movement pattern and pupillary analysis such as pupil size, gradient of pupil size variation, fixation length/count for the area of interest. The proposed model identifies the human's implicit intention into three types such as navigational intent generation, informational intent generation, and informational intent disappearance. Navigational intent refers to the search to find something interesting in an input scene with no specific instructions, while informational intent refers to the search to find a particular target object at a specific location in the input scene. In the present study, based on the human eye movement pattern and pupillary analysis, we used a hierarchical support vector machine which can detect the transitions between the different implicit intents - navigational intent generation to informational intent generation and informational intent disappearance.

• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.211-220
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• 2019

Surface electromyogram (sEMG), which is a bio-electrical signal originated from action potentials of nerves and muscle fibers activated by motor neurons, has been widely used for recognizing motion intention of robotic prosthesis for amputees because it enables a device to be operated intuitively by users without any artificial and additional work. In this paper, we propose a training-free unsupervised sEMG pattern recognition algorithm. It is useful for the gesture recognition for the amputees from whom we cannot achieve motion labels for the previous supervised pattern recognition algorithms. Using the proposed algorithm, we can classify the sEMG signals for gesture recognition and the calculated threshold probability value can be used as a sensitivity parameter for pattern registration. The proposed algorithm was verified by a case study of a patient with partial-hand amputation.

• Journal of Biomedical Engineering Research
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• v.34 no.3
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• pp.148-155
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• 2013

This study is about the development of the wrist rehabilitation system for the patient who has limited capability of movement after stroke. Electromyography triggered training system (ETTS) can play the role between complete passive training and patient activating training system. Surface EMG was measured on pronator teres muscle and biceps brachii muscle for wrist pronation and supination. Our system detects whether the subject makes muscular effort for pronation or supination or nothing in every 50 ms. When the effort level exceeds the preset percentage of maximal voluntary contraction, the motor rotates according to the direction of the intention of the subject. EMG triggers the motor rotation for the wrist rehabilitation training until the preset angle. To evaluate its performance, the maximum voluntary contraction level was measured for 4 subjects at first. With the audio-visual instruction to rotate the wrist (pronation or supination) the subjects made effort to follow the instruction. After calculating root mean square (RMS) for 50 ms, the controller determines whether there was muscular effort to rotate while holding the motor. When there was an effort to rotate, the controller rotates the motor 0.8 degree. By comparing the RMS values from two channels of EMG, the controller determines the rotational direction. The onset delay is $0.76{\pm}0.24$ s and offset delay is $0.65{\pm}0.22$ s for pronation. For supination the onset delay is $1.24{\pm}0.41$ s and offset delay is $0.77{\pm}0.22$ s. The system responded fast enough to be used for rehabilitation training. The controller perceived the direction of rotation 100% correctly for the pronation and 97.5% correctly for supination. ETTS was developed and the fundamental functions were validated for normal subjects. The clinical validation should be done with patients for real world application. With ETTS, the subjects can train voluntarily over the limitation of the range of motion which increases the effectiveness of the rehabilitation training.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.197-206
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• 2014

Motor imagery based Brain-computer Interface(BCI), which has recently attracted attention, is the technique for decoding the user's voluntary motor intention using Electroencephalography(EEG). For classifying the motor imagery, event-related desynchronization(ERD), which is the phenomenon of EEG voltage drop at sensorimotor area in ${\mu}$-band(8-13Hz), has been generally used but this method are not free from the performance degradation of the BCI system because EEG has low spatial resolution and shows different ERD-appearing band according to users. Common spatial pattern(CSP) was proposed to solve the low spatial resolution problem but it has a disadvantage of being very sensitive to frequency-band selection. Discriminative filter bank common spatial pattern(DFBCSP) tried to solve the frequency-band selection problem by using the Fisher ratio of the averaged EEG signal power and establishing discriminative filter bank(DFB) which only includes the feature frequency-band. However, we found that DFB might not include the proper filters showing the spatial pattern of ERD. To solve this problem, we apply a band-selection process using CSP feature vectors and linear discriminant analysis to DFBCSP instead of the averaged EEG signal power. The filter selection results and the classification accuracies of the existing and the proposed methods show that the CSP feature is more effective than signal power feature.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.12
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• pp.1141-1146
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• 2014

As life expectancy becomes longer, reduction of human muscular strength threatens quality of human life. Many robotic devices have thus been developed to support and help human daily life. This paper deals with a new type of in-wheel actuator that can be effectively used for the robotic devices. BLDC motor, drive board, brake, ARS (Attribute Reference System), and torque sensor are combined in the single actuator module. The torque sensor is used to recognize human intention and the in-wheel actuator drives walking aids in our system. Its feasibility was tested with the active walking aid device equipped with the in-wheel actuator. Based on it, we designed an admittance filter algorithm to react on uphill and downhill drive. By adjusting mass, damping, and spring parameters in accordance with the ARS output, it provided convenient drive to the old on uphill and downhill walks.

• Journal of KIISE
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• v.44 no.9
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• pp.887-892
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• 2017

Brain-computer interface (BCI) is a technology that controls computer and transmits intention by measuring and analyzing electroencephalogram (EEG) signals generated in multi-channel during mental work. At this time, optimal EEG channel selection is necessary not only for convenience and speed of BCI but also for improvement in accuracy. The optimal channel is obtained by removing duplicate(redundant) channels or noisy channels. This paper propose a dual filter-based channel selection method to select the optimal EEG channel. The proposed method first removes duplicate channels using Spearman's rank correlation to eliminate redundancy between channels. Then, using F score, the relevance between channels and class labels is obtained, and only the top m channels are then selected. The proposed method can provide good classification accuracy by using features obtained from channels that are associated with class labels and have no duplicates. The proposed channel selection method greatly reduces the number of channels required while improving the average classification accuracy.

• Journal of Digital Convergence
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• v.14 no.10
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• pp.119-128
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• 2016

This study is designed with an intention to understand CSR story strategies that the corporates use, focusing on analyzing the method of composition of Hyundai Motor Company's CSR website stories. When analyzing based on interactivity, ease of use, newest, and informativity, interactive dialogue feature was mostly lacking. It is not the most up-to-date data and lacks the newest. However, as sharing information feature was presented, information spread quickly. When applying Greimas' Actantial Model into the 'CSR News' that conveys the news about corporate philanthropic activities, it turned out that the CSR strategies were authenticity, consistency and flexibility. When doing CSR storytelling, a corporate should not only use pre-existing executives and staff members but should use new icons including civic organizations and the youth if possible, and perform its role as a supporter. At the same time, a corporate must build strategical storytelling to the new values and engage in systematic corporate philanthropic activities that meets the need of the time period.

• The Journal of Korean Physical Therapy
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• v.23 no.5
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• pp.65-72
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• 2011

Purpose: The intention of this study was to investigate the effects of modified constraint-induced movement therapy (mCIMT) with bilateral arm training (BAT) on the motor performance and daily activity performance of individuals with chronic hemiparetic stroke. Methods: Sixteen subjects one year after stroke participated in this study with a control group; the pretest-posttest method was used. The subjects were randomly allocated into two groups: combination of bilateral arm training and modified constraint-induced movement therapy (n=8), and modified constraint-induced movement therapy (n=8). The mCIMT group received therapy for 90 minutes in 3 sessions per week over a period of 4 weeks. The patients receiving a combination of mCIMT and BAT were treated for the same period and frequency. The results were evaluated using the Fugl-Meyer Assessment, Action Research Arm Test (ARAT), and Motor Activity Log-Amount of Use, and Quality of Movement (MAL-AOU, QOM) assessment tools. Results: The Fugl-Meyer Assessment showed that hand and wrist performance improved significantly more in the mCIMT group than in the Combination group (p<0.05). Result from the ARAT assessment showed greater scores for gross movement in the combined group than in the mCIMT group (p<0.05). The MAL-AOU showed that there was greater improvement in the combined group than in the mCIMT group (p<0.05). Conclusion: The forced use of the more affected side can be important for the enhancement of upper extremity performance for chronic hemiparetic stroke patients during their daily activities.

• Korean Institute of Interior Design Journal
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• v.20 no.4
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• pp.101-108
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• 2011

When observing an object in a space, a part of it is remembered into our perception in the time for paying attention or conscious observation and it reaches to our visual understanding. In this study, it examined characteristics by each subject through the process of visual understanding by changes in such observation time. The results from this study are summarized as belows: First, through analysis of the observation data focused on the distance between the observed points, it was able to apply those visual theories organized before to the analysis of characteristics of the time for understanding by each subject. Second, there showed big differences in the time for visual understanding by each subject according to changes in the observation time so that it was found that there were big differences according to the characteristics of subject's intention or purpose of the observation of a space. Third, as the number of continuous observation gives an important clue in judgement of how well the space was understood, it was able to compare and organize the mutual characteristics of the time the attention was concentrated, the time observed intentionally and the time understood visually. Fourth, it was found that the shorter subjects gave the intentional observation in observing a space, the longer they spent the time for paying attention, while the less they could understand it visually.