• International journal of advanced smart convergence
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• v.6 no.3
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• pp.29-37
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• 2017

The current behavior recognition system don't match data formats between sensor data measured by user's sensor module or device. Therefore, it is necessary to support data processing, sharing and collaboration services between users and behavior recognition system in order to process sensor data of a large capacity, which is another formats. It is also necessary for real time interaction with users and behavior recognition system. To solve this problem, we propose fog cloud based behavior recognition system for human body sensor data processing. Fog cloud based behavior recognition system solve data standard formats in DbaaS (Database as a System) cloud by servicing fog cloud to solve heterogeneity of sensor data measured in user's sensor module or device. In addition, by placing fog cloud between users and cloud, proximity between users and servers is increased, allowing for real time interaction. Based on this, we propose behavior recognition system for user's behavior recognition and service to observers in collaborative environment. Based on the proposed system, it solves the problem of servers overload due to large sensor data and the inability of real time interaction due to non-proximity between users and servers. This shows the process of delivering behavior recognition services that are consistent and capable of real time interaction.

• Journal of the Korea Fashion and Costume Design Association
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• v.16 no.1
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• pp.77-89
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• 2014

This study looked into the effect of the life style of single females in 20s and 30s on beauty behavior recognition, and spss 17.0 is used for data analysis method. As for the statistical analysis method in order to validate the measurement tools, reliability verification is conducted and life style groups are sampled using K-means taking into account factor scores by life style. To find out the difference between general beauty behavior recognition and life style, descriptive statistics and One Way ANOVA were carried out, and Duncan Test was implemented for the post examination method. Multiple regression analysis was also carried out to figure out the effect of life style on beauty behavior recognition. The result is as follows. First, according to the results of reliability verification and factor analysis for the lifestyle type and the recognition of the behavior for beauty, the types of the life style of the subjects were divided into Economic Utility, Convention Conservatism, Self Development, Showy Consumption, and Appearance Oriented, and the recognition of the behavior for beauty was named as Makeup and Hair, Cosmetic Surgery, Body Care, and Skin Care. Second, as to the recognition of the behavior for beauty based upon the lifestyle, the Appearance Oriented in Showy Consumption recorded the highest. Third, the analysis of the influence of the style on the recognition of the behavior for beauty showed that the behavior recognition for Makeup and Hair and for Skin Care was affected by the life style of Self Development, Showy Consumption, and Appearance Oriented; the behavior recognition for Cosmetic Surgery was affected by the life style of Conventional Conservatism, Showy Consumption, and Appearance Oriented; and again the behavior recognition for Body Care was by that of Economical Utility and Showy Consumption.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.807-818
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• 2022

In order to further improve the accuracy and time efficiency of behavior recognition in intelligent monitoring scenarios, a human behavior recognition algorithm based on YOLO combined with LSTM and CNN is proposed. Using the real-time nature of YOLO target detection, firstly, the specific behavior in the surveillance video is detected in real time, and the depth feature extraction is performed after obtaining the target size, location and other information; Then, remove noise data from irrelevant areas in the image; Finally, combined with LSTM modeling and processing time series, the final behavior discrimination is made for the behavior action sequence in the surveillance video. Experiments in the MSR and KTH datasets show that the average recognition rate of each behavior reaches 98.42% and 96.6%, and the average recognition speed reaches 210ms and 220ms. The method in this paper has a good effect on the intelligence behavior recognition.

• Journal of the Korean Home Economics Association
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• v.36 no.1
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• pp.99-116
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• 1998

The purposes of this study were to find the influence of wife's home management behavior pattern and husband's perception about it on family life satisfaction, and to find out variables which influence them. For theses reviewing literature and empirical research were conducted. The major results were as follows; 1) The couple's psychological variables (ie, degree of life level recognition, of resourcefulness recognition and of communication) were relatively high. The wife's home management behavior pattern was relatively morphogenesis and the husband's perception about wive's it was relatively morphogenesis. And the couple's degree of family life satisfaction were relatively high. 2) Influential variables on wife's home management behavior pattern were level of education, degree of resourcefulness recognition and of communication. And influential variables on husband's perception about vive's it was degree of communication. 3) Influential variables on wive's the degree of family life satisfaction were degree of life level recognition, of resourcefulness recognition and of communication. And influential variables on husband's it were level of education, job, degree of life level recognition, of resourcefulness recognition and of communication. 4) The wife's home management behavior pattern and husband's perception about wive's it were to predict the couple's degree of family life satisfaction. 5) In cause-effect pathway mode. level of education·job·degree of life level recognition·of resourcefulness recognition and of communication showed direct and indirect effect on family life satisfaction through wife's home management behavior pattern or husband's perception about wive's it.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• 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.

• Journal of Korean Academy of Rural Health Nursing
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• v.15 no.2
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• pp.41-48
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• 2020

Purpose: This study was a descriptive study to identify the factors affecting the dementia prevention behavior of elders in rural communities. Methods: The participants in this study were 125 elders aged 60 or older who lived in Eup or Myeon areas of P city. For data analysis, SPSS/WIN 22.0 was used to perform descriptive statistics, t-test, ANOVA, Pearson correlation, and linear multiple regression and mediated effects. Results: Scores for dementia recognition, dementia attitude and dementia prevention behavior averaged 5.6±2.50 points in the 0~11 range, 38.8±4.59 in 14~56 and 20.2±3.59 in 10~30 respectively. Dementia recognition (a), dementia attitude (b), dementia prevention behavior (c) and the number of chronic diseases of the elders (d) were positively or negatively correlated with each other (rab=.29, p<.01; rbc=.26, p<.01; rac=.36, p<.01; rad=-.29, p<.01; rcd=.19, p<.05). Factors affecting dementia prevention behavior were dementia recognition, dementia attitude, and degree of dementia interest. When the number of chronic diseases affects dementia prevention behavior, dementia recognition has a mediating effect. Conclusion: In order to prevent dementia among elders in rural areas, appropriate management of chronic diseases and provision of appropriate dementia-related education and information to enhance dementia recognition should be provided.

• Korean Journal of Human Ecology
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• v.22 no.5
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• pp.407-417
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• 2013

The purpose of this study was to investigate and analyze how teachers' recognition of professionalism and the child's gender and term care affect child's social interaction behavior. Participants were three-year-old 61 children and their 20 teachers. Each child was observed by the time sampling method of 20 sec-observation followed by 10 sec-recording for a total of 14 minutes. The teachers completed the rating scales to measure the teachers' recognition of professionalism. The study results show that, children engaged more frequently in individual behavior than in interactions with peers or with teachers in day care centers. And those children had more interaction behavior with their teachers than with their peers. Correlation between teachers' recognition of professionalism and children's social interaction behavior were as following: the more the teachers recognized professionalism, the more the children showed positive interaction behavior toward their teachers. Also, the more the teachers recognized the professionalism related to the job satisfaction, the more the children showed positive interaction behavior toward their peers. Boys interacted more negatively with peers and teachers than girls did. Children who attended the day care center more than two years showed less individual behaviors than others.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.123-129
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• 2014

In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.

• Korean Journal of Community Nutrition
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• v.21 no.1
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• pp.75-83
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• 2016

Objectives: The purpose of this study was to analyze correlation thresholds and assessment for salty taste and high-salt dietary behaviors by age. Methods: A total of 524 subjects including 100 each of elementary school students, middle school students, college students, and elderly as well as 124 adults were surveyed for detection and recognition thresholds, salty taste assessments, and high-salt dietary behaviors. Results: Elementary students had a lower detection threshold (p<0.05) and recognition threshold (p<0.01) than did the other groups. Salty taste assessments were lowest among elementary students, followed by middle school students, while college students, adults, and elderly had higher assessment score (p<0.001). Elementary students had significantly lower scores for high-salt dietary behavior than did middle school students, college students, adults and elderly (p<0.001). Middle school students had higher scores for high-salt dietary behavior than did elementary school students and elderly (p<0.001) but no meaningful difference was found in dietary behavior scores between college students, adults, and elderly. There were positive correlations between high-salt dietary behavior and detection thresholds (p<0.001), recognition thresholds (p<0.001), and salty taste assessment (p<0.001). High-salt dietary behavior was more positively correlated with salty taste assessment than detection and recognition thresholds for salty taste. Conclusions: This study suggested that salty taste assessments were positively associated with scores for the detection and recognition thresholds and high-salt dietary behavior.