• Korean Journal of Child Studies
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• v.37 no.2
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• pp.1-11
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• 2016

Objective: This study examined how child care teachers' playfulness and recognition of music and movement relate to their teaching intention of music and movement. Methods: Participants were 200 child care teachers in Seoul, Incheon and Gyeonggi areas. The data were analyzed for descriptive statistics, pearson's correlation analysis, hierarchical multiple regression analysis, and sobel test. Results: The main results were as follows: First, child care teachers' playfulness, teaching intention of music and movement and their recognition of music and movement were positively correlated. Second, child care teachers' playfulness influenced on their teaching intention of music and movement. Finally, teachers' recognition of music and movement mediated the relationship between teachers' playfulness and their teaching intention of music and movement. Conclusion: This study showed that teachers' playfulness influenced on their positive recognition of music and movement activities, which was the variable that caused mediation in the teachers' playfulness and their teaching attention.

• Journal of the Korean Home Economics Association
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• v.37 no.3
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• pp.85-100
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• 1999

The purpose of this article is to investigate the causal effects among socio-economic variables, consumer altitudes towards consumerism, the levels of participation in social organization, experience of consumer problems, consumer complaining behavior and the participating intention in consumer movement. The consumer attitudes towards consumerism, the levels of participation in social organization, the consumer complaining behavior exert direct effect on the participating intention in consumer movement. Income, education, age and experience of consumer problems exert indirect effect on the participating intention in consumer movement mediating the intervening variables like the consumer attitudes towards consumerism, the experience of consumer problems, the consumer complaining behavior.

• Science of Emotion and Sensibility
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• v.19 no.1
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• pp.31-38
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• 2016

The brain and muscles both of which are composed of top-down structure occur the connectivity with the change of Electroencephalogram(EEG) and Electromyogram(EMG). In this paper, we studied the difference of functional connectivity between brain and muscles that by applying coherence method to EEG and EMG signals when users exercised upper limb with and without the movement intention. The changes in the EEG and EMG signals were inspected using coherence method. During the upper limb exercise, the mu (8~14 Hz) and beta (15~30 Hz) rhythms of the EEG signal at the motor cortex area are activated. And then the beta and piper (30~60 Hz) rhythms of the EMG signal are activated as well. The result of coherence analysis between EEG and EMG showed the coefficient of active exercise including movement intention is significantly higher than passive exercise. The coherence relations between cognitive response and muscle movement could interpret that the connectivity between the brain and muscle appear during active exercise with movement intention. The feature of coherence between brain and muscles by the status of movement intention will be useful in designing the rehabilitation system requiring feedback depending on the users' movement intention status.

• Journal of KIISE
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• v.42 no.12
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• pp.1544-1550
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• 2015

Recently, the development of practical wearable robot interfaces has resulted in the emergence of wearable robots such as arm prosthetics or lower-limb exoskeletons. In this paper, we propose a novel method of wrist movement intention estimation based on a regression technique using electromyography of human bio-signals. In daily life, changes in user arm position changes cause decreases in performance by modulating EMG signals. Therefore, we propose an estimation method for robust wrist movement intention for arm position changes, combining several movement intention models based on the regression technique trained by different arm positions. In our experimental results, our method estimates wrist movement intention more accurately than previous methods.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.68-79
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• 2016

Electromyographic (EMG) signals have been widely used as motion commands of prosthetic arms. Although EMG signals contain meaningful information including the movement intentions of human body, it is difficult to predict the subject's motion by analyzing EMG signals in real-time due to the difficulties in extracting motion information from the signals including a lot of noises inherently. In this paper, four Ag/AgCl electrodes are placed on the surface of the subject's major muscles which are in charge of four upper arm movements (wrist flexion, wrist extension, ulnar deviation, finger flexion) to measure EMG signals corresponding to the movements. The measured signals are sampled using DAQ module and clustered sequentially. The Fuzzy C-Means (FCMs) method calculates the center values of the clustered data group. The fuzzy system designed to detect the upper arm movement intention utilizing the center values as input signals shows about 90% success in classifying the movement intentions.

• ETRI Journal
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• v.40 no.4
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• pp.522-530
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• 2018

In this study, we developed a robotic walker that actively controls its speed and direction of movement according to the user's gait intention. Sensor fusion between a low-cost light detection and ranging (LiDAR) sensor and inertia measurement units (IMUs) helps determine the user's gait intention. The LiDAR determines the walking direction by detecting both knees, and the IMUs attached on each foot obtain the angular rate of the gait. The user's gait intention is given as the directional angle and the speed of movement. The two motors in the robotic walker are controlled with these two variables, which represent the user's gait intention. The estimated direction angle is verified by comparison with a Kinect sensor that detects the centroid trajectory of both the user's feet. We validated the robotic walker with an experiment by controlling it using the estimated gait intention.

• Korean Journal of Cognitive Science
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• v.25 no.3
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• pp.259-276
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• 2014

Artists such as Duchamp and Balla tried to portray moving objects on static canvases by superimposing snapshots of moving objects. Previously, our group showed the influence of prior experience on brain responses within a motion-sensitive area MT+ to abstr act paintings with or without implied motion. In the present study, we went further to investigate whether the differential MT+activation between observers is originated from differential eye movement patterns. Prior experience was defined operationally with major in art. In addition, we examined whether perceiver's awareness of artist's intention concerning the implied motion, as well as expertise in art, affects the way he/she views the artwork. Results showed that the number and the duration of fixation on the abstract paintings tended to differ between participants based on art major. The awareness of artist's intention was not related to such differences. In contrast, observers' awareness of artist's intention of implying motion affected eye movement patterns in specific regions of the abstract paintings where the motion was portrayed. In other words, observers with awareness focused more on the parts of paintings portraying motion and moved their eyes in the direction corresponding to the direction of moving objects than observers without awareness. Expertise was not related to such specific eye movement patterns. The present study implies that art expertise and awareness of artist's intention play differential roles in observers' perception of paintings with implied motion. Namely, it suggests that expertise is related to the overall perception of paintings, while awareness of implied motion is related to perception of the specific spatial information in those paintings.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.750-769
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• 2022

Indonesia has the largest potential mobile payment (m-payment) market in Southeast Asia. The government has realised this through Government's National Non-Cash Movement to increase its adoption. This is then followed up by advocating its adoption massively. However, for this movement to be a success, ensuring its continued use is critical. Various studies have attempted to contribute to this issue. Incorporating game elements into the application that brings benefits and satisfaction to its user is envisaged as one of the most feasible ways. This study, therefore, sets out to investigate the effects of gamification on the m-payment application, which drive the intention to continued use by employing the Uses dan Gratification Theory (UGT). A total of 826 m-payment users were gathered to be analysed using Structural Equation Modelling. The results show that utilitarian, hedonic and social gratifications have significant effects on the continuance usage intention of m-payment. Theoretically, this study contributes to the literature by showing that gamification applied in the payment significantly affects the m-payment continuance usage intention. Practically, this research informs the m-payment providers to maintain the gamification elements in their applications to ensure their sustainable use. Limitations and future research directions are also discussed.

• 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.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.167-171
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• 2007

Brain computer interface is the technology of interface for next generation. Recently, user intention has been tried to be recognized for interfacing a computer. EEG plays important role in developing practical application in this area. Much research has focused on extracting EEG commander generated by human movement. ERD/ERS has generally accepted as important EEG parameters for prediction of human movement. However, There has been difference between initial movement indicated by ERD/ERS and real movement. Therefore, this study was to determine the time differences for brain interface by ERD/ERS. Five university students performed ten repetitive movements. ERD/ERS was determined according to movement execution and the significant pattern showed the difference between movement execution and movement indication of ERD/ERS.