• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.83-86
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• 2003

This paper suggests user's interface with computer by means of detecting gaze under HMD, head mounted display, environment. System is derived as follows; firstly, calibrate a camera in HMD, which determines geometrical relationship between monitor and captured image. Second, detect the center of pupil using algorithm of the center of mass and represent a gazing position on the computer screen. If user blinks or stares at a certain position for a while, message is sent to computer. Experimental results show the center of mass is robust against glint effects, and detecting error was 7.1%. and 4.85% in vertical and horizontal direction, respectively. To adjust detailed movement of a mouse takes 0.8 sec more. The 98% of blinking is detected successfully and 94% of clicking detection is resulted.

• Smart Media Journal
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• v.5 no.3
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• pp.25-29
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• 2016

This paper presents research for non-contact gesture recognition system using EPS(Electronic Potential Sensor) for measuring the human body of electromagnetic fields. It implemented a signal acquisition and signal processing system for designing a system suitable for motion recognition using the data coming from the sensors. we transform AC-type data into DC-type data by applying a 10Hz LPF considering H/W sampling rate. in addition, we extract 2-dimensional movement information by taking difference value between two cross-diagonal deployed sensor.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.60-65
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• 2012

This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.

• International Journal of Contents
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• v.16 no.1
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• pp.75-86
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• 2020

As the elderly population is becoming an aging society, the elderly are experiencing many problems. Social security costs for the elderly are increasing and the un-linked social phenomenon is emerging. Thus, the social infrastructure and welfare system established in the past economic growth period are in danger of not functioning properly. People socially isolated or with chronic diseases among the elderly are exposed to various accidents. Thus, an active healthcare management service is imperative. Additionally, in the event of a dangerous situation, the system must have ways to notify guardians (family or medical personnel) regarding appropriate action. Thus, in this paper, we propose the smartphone-based healthcare and emergency response service platform. The proposed service platform aggregates movement of relevant data in real-time using a smartphone. Based on aggregated data, it will always recognize the user's movements and current state using the human motion recognition mechanism. Thus, the proposed service platform provides real-time status monitoring, activity reports, a health calendar, location-based hospital information, emergency situation detection, and cloud messaging server-based efficient notification to several subscribers such as family, guardians, and medical personnel. Through this service, users or guardians can augment the level of care for the elderly through the reports. Also, if an emergency situation is detected, the system immediately informs guardians so as to minimize the risk through immediate response.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.290-293
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• 2021

In this paper, we implement a learning efficiency verification system to inspire learning motivation and help improve concentration by detecting the situation of the user studying. To this aim, data on learning attitude and concentration are measured by extracting the movement of the user's face or body through a real-time camera. The Jetson board was used to implement the real-time embedded system, and a convolutional neural network (CNN) was implemented for image recognition. After detecting the feature part of the object using a CNN, motion detection is performed. The captured image is shown in a GUI written in PYQT5, and data is collected by sending push messages when each of the actions is obstructed. In addition, each function can be executed on the main screen made with the GUI, and functions such as a statistical graph that calculates the collected data, To do list, and white noise are performed. Through learning efficiency checking system, various functions including data collection and analysis of targets were provided to users.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.133-141
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• 2006

Recently many wearable computers have been developed. But they still have many user interface problems from both an input and output perspective. This paper presents a wearable user interface based on EOG(electrooculogram) sensing circuit and marker recognition. In the proposed user interface, the EOG sensor circuit which tracks the movement of eyes by sensing the potential difference across the eye is used as a pointing device. Objects to manipulate are represented human readable markers. And the marker recognition system detects and recognize markers from the camera input image. When a marker is recognized, the corresponding property window and method window are displayed to the head mounted display. Users manipulate the object by selecting a property or a method item from the window. By using the EOG sensor circuit and the marker recognition system, we can manipulate an object with only eye movement in the wearable computing environment.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.20-27
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• 2021

Eye tracking is one of the NUI technologies, and it finds out where the user is gazing. This technology allows users to input text or control GUI, and further analyzes the user's gaze so that it can be applied to commercial advertisements. In the eye tracking system, the allowable range varies depending on the quality of the image and the degree of freedom of movement of the user. Therefore, there is a need for a method of estimating the accuracy of eye tracking in advance. The accuracy of eye tracking is greatly affected by how the eye tracking algorithm is implemented in addition to hardware variables. Accordingly, in this paper, we propose a method to estimate how many degrees of gaze changes when the pupil center moves by one pixel by estimating the maximum possible movement distance of the pupil center in the image.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.20-28
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• 2010

In this paper, we propose a hand gesture interface for the manipulation of augmented objects in 3D space using a camera. Generally a marker is used for the detection of 3D movement in 2D images. However marker based system has obvious defects since markers are always to be included in the image or we need additional equipments for controling objects, which results in reduced immersion. To overcome this problem, we replace marker by planar hand shape by estimating the hand pose. Kalman filter is for robust tracking of the hand shape. The experimental result indicates the feasibility of the proposed algorithm for hand based AR interfaces.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.3
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• pp.171-176
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• 2013

In this paper, we present a MEMS (micro-electro-mechanical system) implantable blood pressure sensor which has designed and fabricated with consideration of size, design flexibility, and wireless detection. Mechanical and electrical characterizations of the sensor were obtained by mathematical analysis and computer aided simulation. The sensor is composed of two coils and a air gap capacitor formed by separation of the coils. Therefore, the sensor produces its resonant frequency which is changed by external pressure variation. This frequency movement is detected by inductive coupling between the sensor and an external antenna coil. Theoretically analyzed resonant frequency of the sensor under 760 mmHg was calculated to 269.556 MHz. Fused silica was selected as sensor material with consideration of chemical and electrical reaction of human body to the material. $2mm{\times}5mm{\times}0.5mm$ pressure sensors fitted to radial artery were fabricated on the substrates by consecutive microfabrication processes: sputtering, etching, photolithography, direct bonding and laser welding. Resonant frequencies of the fabricated sensors were in the range of 269~284 MHz under 760 mmHg pressure.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.