• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.374-379
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• 2002

In this study, we developed a new human arm movement detection system using electrical bio-impedance method with several skin-electrodes. The correlation coefficients of the joint angle and the impedance change from human arm movement was obtained using a goniometer and impedance measurement system developed in this study. The correlation coefficients of the wrist and the elbow movements were 0.94 and -0.99, respectively. This system was applied to control a robotic arm by converting the measured impedance to joint angle to confirm the validity of the proposed system. In conclusion, we confirmed that this system can control the robotic arm according to arm movement without any limitation of movement. This system showed possibility that upper arm movement could be easily measured by impedance measurement system with a few skin-electrodes.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.11a
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• pp.269-272
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• 2009

The environment of Ubiquitous in terms of improvement is being expanded to various fields and time enabled system. Thus, a real-time spatial information management system has been developed by integrating a human movement detection system into a SICS(Spatial Information Control System) engine that can integrally manage inside spatial information extracted from 3D CAD and outside spatial information of GIS. The add-on program was developed to extract spatial information necessary for the SICS engine from 3D CAD information, and a human movement detection system was developed. Test bed was operated for 2weeks and indoor human flow information was found out by zone. Also, the direction of future research was decided through a test bed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.857-860
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• 2014

This paper proposes a human movement detection system by a thermopile array sensor. In the system, the sensor is attached to the ceiling and it acquires spatial temperatures, which is called thermal distribution. The system obtains $4{\times}4$ pixels thermal distributions from the sensor. The distributions are analyzed to extract human movement. As the experimental result, the proposed system successfully detected human movements.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.88-95
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• 2022

Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clustering-based anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.5
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• pp.481-488
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• 2010

Most public guide robots installed in public spots such as exhibition halls and lobbies of department store etc., have poor capability to distinguish the users who require services. As to provide suitable services, public guide robots should have a human detection system that makes it possible to evaluate intention of customers from their movement direction. In this paper, a DPS (Directional Perception System) is realized based on face detection technology. In particular, to catch human movement efficiently and reduce computational time, human detection technology using face rectangle, which is obtained from the human face, is developed. DPS determines which customer needs services of public guide robots by investigating the size and direction of face rectangle. If DPS is adapted, guide service will be provided with more satisfaction and reliability, and power efficiency also can be added up because public guide robots provide services only for the users who expresses their intentions of wanting services explicitly. Finally, through several experiments, the feasibility of the proposed DPS is verified.

• Journal of The Korean Digital Architecture Interior Association
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• v.8 no.2
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• pp.13-20
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• 2008

As buildings have been constructed higher and more complicated recently, the activities of the residents occurred in those multiplex buildings have also become more various. As a result, possibility and the size of the damage from the disaster like a fire are getting larger. So, many studies for preventing the damage in refuge situation are being conducted. In this study, a new process for finding the optimum refuge path is presented, which is different from existing methods. This new method operates by using the human movement detection system in the building for real time. And the process also shows the new way which can shorten the number of calculation for deciding the optimum refuge path. That new way is to make variables such as the velocity of smoke and person movement into a constant. Finally it will be applied to a multiplex building.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.8
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• pp.506-509
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• 2003

This paper describes the possibility of analyzing gait pattern from the changes of the lower leg electrical impedance. This impedance was measured by the four-electrode method. Two current electrodes were applied to the thigh, knee, and foot., and two potential electrodes were applied to the lateral, medial, and posterior position of human leg. The correlation coefficients of the joint angle and the impedance change from human leg movement was obtained using a electrogoniometer and 4ch impedance measurement system developed in this study. We found the optimal electrode position for knee and ankle joint movements based on high correlation coefficient, least interference, and maximum magnitude of impedance change. The correlation coefficients of the ankle, knee, and the hip movements were -0.913, 0.984 and 0.823, respectively. From such features of the human leg impedance, it has been made clear that different movement patterns exhibit different impedance patterns and impedance level. This system showed feasibility that lower leg movement could be easily measured by impedance measurement system with a few skin-electrodes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1421-1431
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• 2005

There are two methods in 3-D sound reproduction: a surround system, like 3.1 channel method and a binaural system using 2-channel method. The binaural system utilizes the sound localization principle of a human using two ears. Generally, a crosstalk between each channel of 2-channel loudspeaker system should be canceled to produce a natural 3-D sound. To solve this problem, it is necessary to trace a head movement. In this paper, we propose a new algorithm to correctly trace the head movement of a listener. The Proposed algorithm is based on the detection of face and eye. The face detection uses the intensity of an image and the position of eyes is detected by a mathematical morphology. When the head of the listener moves, length of borderline between face area and eyes may change. We use this information to the tracking of head movement. A computer simulation results show That head movement is effectively estimated within +10 margin of error using the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3037-3054
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• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.