• Asia pacific journal of information systems
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• v.27 no.1
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• pp.38-53
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• 2017

As sensor technologies and image processing technologies make collecting information on users' behavior easy, many researchers have examined automatic emotion recognition based on facial expressions, body expressions, and tone of voice, among others. Specifically, many studies have used normal cameras in the multimodal case using facial and body expressions. Thus, previous studies used a limited number of information because normal cameras generally produce only two-dimensional images. In the present research, we propose an artificial neural network-based model using a high-definition webcam and Kinect to recognize users' emotions from facial and bodily expressions when watching a movie trailer. We validate the proposed model in a naturally occurring field environment rather than in an artificially controlled laboratory environment. The result of this research will be helpful in the wide use of emotion recognition models in advertisements, exhibitions, and interactive shows.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.982-990
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• 2018

In recent years, many wearable devices and mobile apps related to life care have been developed, and a service for measuring the movement during walking and showing the amount of exercise has been provided. However, they do not measure walking in detail, so there may be errors in the total calorie consumption. If the user's behavior is measured by a multi-axis sensor and learned by a machine learning algorithm to recognize the kind of behavior, the detailed operation of walking can be autonomously distinguished and the total calorie consumption can be calculated more than the conventional method. In order to verify this, we measured activities and created a model using a machine learning algorithm. As a result of the comparison experiment, it was confirmed that the average accuracy was 12.5% or more higher than that of the conventional method. Also, in the measurement of the momentum, the calorie consumption accuracy is more than 49.53% than that of the conventional method. If the activity recognition is performed using the wearable device and the machine learning algorithm, the accuracy can be improved and the energy consumption calculation accuracy can be improved.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• Women's Health Nursing
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• v.4 no.2
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• pp.245-257
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• 1998

The characteristics of health behavior related pregnancy and childbirth have reflected on the cultural belief and value in the society. The efforts for women's health promotion through the current illumination of the traditional health care are the prompting assignment to be in nursing. The process of motherhood already progress before the motherhood actually. The functional state as the expectant mother can be the important predicting factor of the postpartum state, the quality of a married life. Motherhood was analyzed by Walker and Avant's method to clarify the concept 'to be a mother' using the various concepts like Koreans' Taekyo, transition to motherhood, maternal identity, maternal role attainment, maternal fetal attachment, and maternal fetal interaction. Upon the concept analysis, naturalness, responsibility, attachment, readiness, controllability were identified as the defining characteristics of motherhood. The antecedents of motherhood were consist of maternal affection, positive self esteem, pregnancy acceptance, fetus recognition and the consequences of motherhood were consist of positive maternal identity, maternal fetal attachment, confidence about the maternal role, the healthy mother and the healthy baby. The empirical referents of motherhood were consists of recognition of motherhood, expectation about motherhood, fetal recognition with ultrasonography and fetal movement, experience of unification between mother and fetus, expression of affection to the fetus, concern about fetal health, concern and practice about Taekyo, adaptation behavior about physical change and discomfort due to pregnancy. Therefore it is necessary to develop the instruction program of motherhood including the defining attributes identified in this study.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1062-1070
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• 2003

In this paper, we observe one of the aquatic insect, fish(Medaka)'s behavior which reacts to giving toxic chemicals until lethal conditions using automatic tracking sl$.$stem. For the result, we define the Pattern A is a normal movement of fish and Pattern B is after giving the chemicals. In order to detect the movement of fish automatically, these patterns are selected for the training data of the artificial neural networks. The average recognition rates of the pattern B are remarkably increased after inputs of toxic chemical(diazinon) while the Pattern A is decreased distinctively. This study demonstrates that artificial neural networks are useful method for detecting presence of toxicoid in environment as for an alternative of in-situ behavioral monitoring tool.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.175-178
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• 2015

This paper presents a VR-simulated sailor training platform for emergency in order to prevent a human error that causes 60~80% of domestic/ abroad marine accidents. Through virtual reality technology, the proposed platform provides an interaction method for proficiency of procedures in emergency, and a crowd control method for controlling crowd agents in a virtual ship environment. The interaction method uses speech recognition and gesture recognition to enhance the immersiveness and efficiency of the training. The crowd control method provides natural simulations of crowd agents by applying a behavior model that reflects the social behavior model of human. To examine the efficiency of the proposed platform, a prototype whose virtual training scenario describes the outbreak of fire in a ship was implemented as a standalone system.

• Journal of Distribution Science
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• v.21 no.4
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• pp.103-111
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• 2023

Purpose: The purpose of this study was to analyze Korean sports consumers' intention to stay using online sports products and services through online sports distribution platforms or return to using sports facilities and services in person. Research design, data and methodology: This study set up two models measuring consumers' recognition, attitude, and purchase intention toward online sports products and services based on involvement theory. An online survey was conducted and a total of 2,263 consumers participated in this study. Male participants were 1,256(55.5%) and female participants were 1,007(44.5%). Descriptive statistics were performed, and a path analysis was utilized to analyze the proposed model using SPSS 26 and SAS. Results: The results revealed two proposed models used in this study supported that consumers' online sports product and service recognition leads to a positive attitude toward online sports products and services. Moreover, consumers' positive online sports product and service attitudes were shown to lead to positive intentions to purchase online sports products and services. Conclusions: The findings revealed the recognition of consumers' online sports products and services led to positive attitudes and behavioral intentions. Implications were provided by suggesting the sports industry stick to developing online sports products and services until the endemic of COVID-19 is declared.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2767-2780
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• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

• Journal of Preventive Medicine and Public Health
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• v.25 no.3 s.39
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• pp.269-286
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• 1992

The purpose of this study is to investigate the present state of worker's recognition and attitude about hazard materials and occupational disease in his workplace. In general worker's view of hazard materials and occupational disease that sis recognition and attitude is related to worker's health behavior for preventing occupational disease and improving his health status. The study subject is composed of workers in the asbestos related industry for example brake lining manufacturing industry, asbestos fiber manufacturing industry, and asbestos slate manufacturing industry. The result of the study are follows : 1. The most of workers in the asbestos related industry have taken health education and safety education, and the more than half of workers recognized the usefulness of preventive device, and ventilatory device in workplace. 2. About 70% of workers have always taken the preventive device. 3. About 80% of workers have recognized occupational disease in the asbestos related industry, and about 64% of workers have recognized that hls workplace have harmful effect on his health. 4. Recognition about the usefulness of ventilatory device in work place has not related with any variables. But recognition about the usefulness of repiratory protector has related with recognition of hazard materials in his workplace, for example asbestos. 5. Attitude about severity and susceptability of occupational disease in the asbestos related industry have related with knowledge about hazard materials and occupational disease.