• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.364-369
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• 2001

As the population of persons over the age of sixty-five is rapidly growing, the population of solitary senior person living at own home is growing in Japan. This situation has caused the social issue of how supports their healthy life. There have been some projects related to improve their quality of life and support their healthy life. Unfortunately mostly they focus the method of measuring vital signal and observing behavior. Nobody reports how utilize the measured data. Aim of our project is how find emergency of the aged people at home. As emergency is big different from regular life behavior, we have to recognize it. We propose concept of the human behavior model and show the some types human behavior knowledge constructed by observed human behavior model and show the some types human behavior knowledge constructed by observed human behavior. This idea is based on human having habitual life. And we discuss the possibility of finding emergency using knowledge and observed data.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.491-496
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• 2003

This paper proposes modeling of human behavior and a method of finding irregular human behavior. At first, human behavior model is proposed by paying attention to habitual human behavior at home. Generally, it is difficult to obtain the information of individual life pattern because of high cost for setting sensors such as cameras to observe human action. Therefore we capture turning on/off consumer electronic equipments as actual human behavior action, where some or many consumer electric equipments were used such as television, room light, video and so on in our daily life. Noting that are some relations between turning on/off those consumer electric equipments and our action, we proposes how to construct a human behavior knowledge by analyzing human behavior based on observation of human habitual life. Also an algorithm to identify on find irregular behavior different from habitual life behavior are described. Finally, the significance of the proposed method is shown by some experimental results.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• ETRI Journal
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• v.38 no.6
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1046-1048
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• 2003

Recently, various researches in respect of the positioning technologies using satellites and the other sensors have made location-based services (LBS) more common and accurate. Consequently, concern about position information has been increasing. However, since these positioning systems only focus on user's position, it is difficult to know the user's attitude or detailed behaviors at the specific position. It is worthy to study on how to acquire such human attitude or behavior, because those information is useful to know the context of the user. In this paper, the sensor unit consisting of three dimensional accelerometer was attached to human body, and autonomously measured the perpendicular acceleration of ordinary human behaviors including activity modes such as walking, running, and transportation mode using transportation such as a train, a bus, and an elevator. Subsequently, using the classified measurement results, the method to identify the human activity modes was proposed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2314-2318
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• 2010

For human-robot interaction, a robot should recognize the meaning of human behavior. In the case of static behavior such as face expression and sign language, the information contained in a single image is sufficient to deliver the meaning to the robot. In the case of dynamic behavior such as gestures, however, the information of sequential images is required. This paper proposes behavior classification by using fuzzy classifier to deliver the meaning of dynamic behavior to the robot. The proposed method extracts feature points from input images by a skeleton model, generates a vector space from a differential image of the extracted feature points, and uses this information as the learning data for fuzzy classifier. Finally, we show the effectiveness and the feasibility of the proposed method through experiments.

• Management & Information Systems Review
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• v.4
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• pp.201-224
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• 2000

Since Organ(1977) published a paper on the importance of organizational citizenship behavior, studies of organizational citizenship behavior have increased continuously. An exploratory study on the application strategy of organizational citizenship behavior for human resource management is very scarce. Many organizational researchers so far, have focused on investigating in-job behavior. Yet, from a decade, organizational researchers have recognized that in-job behavior alone can not explain sufficiently job performance or organizational effectiveness. Thus, they have paid attention to extra-job behavior, which is generally called as organizational citizenship behavior. Focusing on the importance of human resource management in organizational citizenship behavior, this paper is to examine an exploratory study on the application strategy of organizational citizenship behavior.

• Journal of Families and Better Life
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• v.6 no.1
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• pp.95-116
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• 1988

this study has dual purposes; one is to develope a new theoretical framework in consumer behavior area by applying the human ecological approach, and the other is to test the theory empirically area by applying the human ecological approach, and the other is to test the theory empirically by examining prepurchasing behavior of housing. Research methods adopted in this study are library search and survey research with self-administered questionnaires. The statistical methods used in the survey research are factor analysis, chi square test, and multivariate analysis with crosstablulations. According to the human ecological approach, ecological environments are important sources of consumer needs which , in turn, are satisfied by purchasing behavior in the market. Within this theoretical framework, consumers con improve the quality to life by perceving clearly what their needs are thereby making the most possible efficient purchasing decision making. The major findings of the empirical research on the basis of the theoretical framework are as follows; 1) Housing needs significantly vary with different ecological environment. 2) consumer information search behavior does not differ significantly by housing needs. 3) Housing needs turn out to be an intervening variable between ecological environments and consumer information search behavior. the results of this study show that the human ecological approach is useful in consumer behavior studies. The empirical result that consumer needs are not significantly satisfied by consumer behavior suggests a now direction in consumer education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

• Nuclear Engineering and Technology
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• v.41 no.3
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• pp.247-270
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• 2009

This paper reviews studies on information searching behavior in process control systems and discusses some implications learned from previous studies for use in human factors studies on nuclear power plants (NPPs) main control rooms (MCRs). Information searching behavior in NPPs depends on expectancy, value, salience, and effort. The first quantitative scanning model developed by Senders for instrument panel monitoring considered bandwidth (change rate) of instruments as a determining factor in scanning behavior. Senders' model was subsequently elaborated by other researchers to account for value in addition to bandwidth. There is also another type of model based on the operator's situation awareness (SA) which has been developed for NPP application. In these SA-based models, situation-event relations or rules on system dynamics are considered the most significant factor forming expectancy. From the review of previous studies it is recommended that, for NPP application, (1) a set of symptomatic information sources including both changed and unchanged symptoms should be considered along with bandwidth as determining factors governing information searching (or visual sampling) behavior; (2) both data-driven monitoring and knowledge-driven monitoring should be considered and balanced in a systematic way; (3) sound models describing mechanisms of cognitive activities during information searching tasks should be developed so as to bridge studies on information searching behavior and design improvement in HMI; (4) the attention-situation awareness (A-SA) modeling approach should be recognized as a promising approach to be examined further; and (5) information displays should be expected to have totally different characteristics in advanced control rooms. Hence much attention should be devoted to information searching behavior including human-machine interface (HMI) design and human cognitive processes.