• 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.

• International Journal of Automotive Technology
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• v.7 no.1
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• pp.35-41
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• 2006

Driving simulations were performed to evaluate the effect of providing both visual information and body sensory information on changes in steering characteristics and the amount of perspiration in drift cornering. When the driver is provided with body sensory information and visual information, the amount of perspiration increases and the driver can perform drift control with a moderate level of tension. With visual information only, the driver tends to easily go into a spin because drift control is difficult. In this case, the amount of perspiration increases greatly as compared with the case where body sensory information is also provided, reflecting a very high perception of risk. When body sensory information is provided, the driver can control drift adequately, feeding back the roll angle information in steering. The importance of the driver's perception of the state of the vehicle was thus confirmed, and a desirable future direction for driver assistance systems was determined.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.91-101
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• 2008

This paper proposes component-based density propagation for tracking a component-based human body model that comprises components and their flexible links. We divide a human body into six body parts as components - head, body, left arm, right arm, left foot, and right foot - that are most necessary in tracking its movement. Instead of tracking a whole body's silhouette, using component-based density propagation, the proposed method individually tracks each component of various parts of human body through a human body model connecting the components. The proposed human body tracking system has been applied to track movements usee for young children's movement education: balancing, hopping, jumping, walking, turning, bending, and stretching. This proposed system demonstrated the validity and effectiveness of movement tracking by independently detecting each component in the human body model and by acquiring an average 97% of high tracking rate.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.110-116
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• 2023

Recently, as various examples of machine learning have been applied in the healthcare field, deep learning technology has been applied to various tasks, such as electrocardiogram examination and body composition analysis using wearable devices such as smart watches. To utilize deep learning, securing data is the most important procedure, where human intervention, such as data classification, is required. In this study, we propose a model that uses a clustering algorithm, namely, the K-means clustering, to label body fat according to gender and age considering body size aspects, such as chest circumference and waist circumference, and classifies body fat into five groups from high risk to low risk using a convolutional neural network (CNN). As a result of model validation, accuracy, precision, and recall results of more than 95% were obtained. Thus, rational decision making can be made in the field of healthcare or obesity analysis using the proposed method.

• Journal of IKEEE
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• v.26 no.1
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• pp.36-42
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• 2022

This study presents an analysis of the human body channel as an electric signal path using body impulse response (BIR). The human body communications (HBC) has recently emerged as an effective signal transmission method to create wireless body area networks (WBAN). We provide body channel characteristics based on measured BIR in a proper experimental environment for the HBC using capacitive coupling with a customized channel sounding device, which can be applied as a guideline for the HBC system design. The frequency response of the BIR, extracted by a customized signal processing for the measure signals, shows the channel path loss (CPS) between 0 MHz and 100 MHz with an average CPS of approximately 46.8 dB. In addition, the relative noise power distributions can provide estimations on the signal to noise ratio at the HBC receiver in terms of capacitor and resistor values in the measured frequency band and the frequency band lower than 3 MHz considering the baseband signal detection.

• 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.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.619-628
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• 2011

Normally vision-based 3D human pose recognition technology is used to method for convey human gesture in HCI(Human-Computer Interaction). 2D pose model based recognition method recognizes simple 2D human pose in particular environment. On the other hand, 3D pose model which describes 3D human body skeletal structure can recognize more complex 3D pose than 2D pose model in because it can use joint angle and shape information of body part. In this paper, we describe a development of interactive game contents using pose recognition interface that using 3D human body joint information. Our system was proposed for the purpose that users can control the game contents with body motion without any additional equipment. Poses are recognized comparing current input pose and predefined pose template which is consist of 14 human body joint 3D information. We implement the game contents with the our pose recognition system and make sure about the efficiency of our proposed system. In the future, we will improve the system that can be recognized poses in various environments robustly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1118-1133
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• 2017

Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people's behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.

• Journal of Korean Medical classics
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• v.25 no.4
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• pp.105-113
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• 2012

Objectives : Acquiring information from symptoms is one of the important method to gain clinically available information in korean medicine. Therefore, up to now, study of symptom terms was frequently implemented in promotion of various information project. In data extraction methods using symptom information from DB, information search using synonym and method using ontology is studied and utilized. However, considering concept of symptom has essential information of appeared body area and phenomenon we think that extending synonym and ontology relationship in symptom terms can be useful for search and set to this study. Methods : We collect terms relevant to human body area and structure described in Donguibogam. Synonymous relationship between collected terms is organized. Relationship between collected terms is build to human-body-knowledge table which has form of Concept+Relation+Concept. Type of relationship is limited on a range of expressing content about parts of human body. Result & Conclusion : Search condition is generated automatically using relationship of the upper area in knowledge table contents. Information of next and previous acupuncture point, upper and lower acupuncture point, left and right acupuncture point can be searched using information of acupuncture point location, order, relative position in area, direction in knowledge table contents.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.204-207
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• 2007

최근 대두되고 있는 무선 센서네트워크는 실생활의 많은 부분에 있어 그 응용 분야를 넓혀 가고 있다. 본 연구는 WSN의 응용 중 Human Health Care에 주안을 두어 WSN을 이용한 원격 진료 시스템에 대해 설계 및 구현을 하였다. 원격 진료 시스템을 위해 각 센서 노드들은 인체의 Body 정보를 수집할 수 있는 센서들을 가지고 신체의 각 부위에 부착된다. 또한 각 센서 노드들은 고유의 Human Body Code를 가지고 있으며 이 고유의 Code에 의해 인체의 어느 부위에서 측정된 Data인지를 Sink 노드로 전송하게 된다. Sink 노드는 수집된 정보를 원격에 위치한 의료진들에게 전송하며 원격의 의료진들은 Sink 노드에서 전송된 정보를 바탕으로 진료 정보를 환자 및 User에게 Feedback하게 된다. Human Body Code는 인체를 세분화하고 각 세분화한 신체 부위에 계층적으로 고유의 Code를 부여한다. 본 연구에서는 실제 Human Body Code를 직접 제작한 센서 Node에 주입하여 Human Body Network을 구성하여 인체에서 센싱되는 Data를 원격에 위치한 PC에서 진료 가능한 원격 진료 시스템을 구현하였다.