Kyung-Yong Chung

Ontology-based healthcare context information model to implement ubiquitous environment

To establish real u-healthcare environments, it is necessary to receive the context information obtained from various platforms at the proper time in portable devices which operate using both wired and wireless communication. Moreover, a knowledge model is required that reflects the information and characteristics needed for such services while remaining appropriate for medical reference. This paper develops an ontology-based healthcare context information model to implement a ubiquitous environment. Contextual information will be extracted and classified to implement the healthcare services using the context information model. The healthcare context information model can be defined using the ontology, and a common healthcare model will be developed by considering medical references and service environments. Application and healthcare service developers can use the sensed information in various environments by authoring device- and space-specific ontologies based on this common ontology. In addition, this paper designs a personalized u-healthcare service system. The validity of the model used in this study is evaluated for the food and exercise recommendation in u-healthcare services.

Item recommendation based on context-aware model for personalized u-healthcare service

A personalized service in the ubiquitous environment is to provide services or items, which reflect personal tastes, attitudes, and contexts. It is impossible to reflect the context information generated in u-healthcare environments due to the existing recommendation system performing the recommendation using the information directly input by users and application usage record only. This study develops a context-aware model using the context information provided by the context information model. The study applies it to the extraction of the missing value in a collaborative filtering process. The context-aware model reflects the information that selects items by users according to the appropriate context using the C-HMM and provides it to users. The solution of the missing value in the preference significantly affects the recommendation accuracy in a preference based item supply method. Thus, this study developed a new collaborative filtering for ubiquitous environments by reflecting the missing preference value and reflecting it to the collaborative filtering using the context-aware model. Also, the validity of this method will be evaluated by applying it to menu services in u-healthcare services.

Home Health Gateway Based Healthcare Services Through U-Health Platform

The Ubiquitous Health, or u-Health, service is an IT health care service using the ubiquitous computing environment. U-Health provides customized medical services. As it is a service that has developed from the current hospital visiting medical system, the u-Health service provides a patient with healthcare anywhere and anytime. In this paper, we propose a home health gateway based healthcare services through the u-Health platform. Using home health gateway, u-Health can provide health monitoring, diet, and exercise services using the healthcare decision support module in the ubiquitous environment. This approach would offer specialized services using an external content provider of DB. In addition, a doctor can provide advice to patients using the monitoring service. The proposed u-Health platform provides effective services using home health gateway in ubiquitous environments to customers, which will improve the health of chronic patients.

Knowledge-based dietary nutrition recommendation for obese management

As the basic paradigm of health management has changed from diagnosis and treatment to preventative management, health improvement and management has received growing attention in societies around the world. Recently the number of obese youth has risen globally and obesity has caused serious problems regarding almost all of the diseases of these days. This study presents dietary nutrition recommendations based on knowledge for obese youth. The knowledge-based dietary nutrition recommendations herein include not only static dietary nutritional data but also individualized diet menus for them by utilizing knowledge-based context data through a collaborative filtering method. The suggested method utilizes the basic information on obese youth, forms a similarity clustering with a high correlation, applies the similarity weight on {user-menu} matrix within the similarity clustering and utilizes the knowledge based collaborative filtering to recommend the dietary nutritional menu. Also by using the knowledge-based context-aware modeling, the study constitutes a {user-menu} merge matrix and solves the sparse problem of previous recommendation system. The suggested method herein, unlike the conventional uniformed dietary nutrition recommendations for obesity management, is capable of providing the personalized recommendations. Also through mobile devices, users can receive personalized recipes and menus anytime and anywhere. By using the proposed method, the researcher develops a mobile application of dietary nutrition recommendation service for obese management. A mobile interface will be built herein and applied in an experiment to test its logical validity and effectiveness.

Emergency situation monitoring service using context motion tracking of chronic disease patients

Nowadays, great attention is paid to studies on fusion health and medical care combined of IT and BT for chronic disease patients due to westernization of dietary life, increase in stress, decrease in physical activities, and others. In reality, full recovery of chronic disease is difficult to achieve as its cause is diverse and complex. Therefore, the necessity for continuous management is proposed rather than approach to treat the disease. It is urgent to come up with the countermeasure since the lengthening of life expectancy in the aging society brings about the increase in chronic diseases and such increased medical expense becomes a big burden in socioeconomic activities. Companies are promoting test-projects in association with health management together with nationwide health management business for chronic diseases. In this study, we proposed the emergency situation monitoring service using context motion tracking for chronic disease patients. Proposed service diagnoses current status of patient based on contextual information collected and it provides information necessary for chronic disease management by analyzing life habits. Bio status recognition can provide proper service through the extraction of contextual data relevant to chronic disease patients. The context motion tracking provides emergency situation monitoring service accordingly with alert and symptom level in case of detecting symptoms through measured results and analysis. Semantic inference engine for context awareness conducts active and intelligent analysis on health condition and life patterns. Since it can properly correspond to extraordinary circumstances, it provides necessary service environment for emergency situation or symptoms. The cameras, speakers, and sensors are installed accordingly with the structure of indoor living space of user and the contextual information is transmitted from them. Considering the user convenience, motion history image is used for the motion recognition and continuous tracking from video. The system detects the patterns of expertise based on life pattern and psychological state through life log based motion detection and provides the service accordingly. It provides health related information and emergency situation monitoring service to user at anytime and anywhere and it is easy to use with simple handling. As a result, this system has the advantage of being able to detect emergency situations realistically and intuitively.

Medical information service system based on human 3D anatomical model

Recently, due to rapid increases in the elderly population, the interest in u-healthcare for personal and social needs is increasing. In addition, extensive medical information through various media services is of interest. However, the general public often has no time to visit a medical authority for u-healthcare. The absence of a system that can be easily and quickly accessed anytime or anywhere to monitor health is a sad reality, especially in light of the rapid development of IT convergence technology. In this paper, we propose a medical information service system that monitors the human body for u-healthcare. First of all, this paper separates the human bodies of an adult male and female into a skeleton, muscle, internal organs, and skin. These four categories are then modeled using 3DS MAX. The human 3D body structures can be viewed with a 3D viewer. One of the key features of this system is the picking or selection technique. If user selects a specific part of the human body in the 3D viewer, the system provides detailed medical information about the diseases associated with the selected part. The 3D viewer has the advantage of being able to view the structure of the human body realistically and intuitively. Medical information about diseases is comprised of simple and clearly organized data concerning the causes, symptoms, treatment, prevention, recommended foods, and related medical institutions (such as hospitals) that can deal with the disease. Thus, our system can prevent diseases in advance and provide answers to many questions about disease-related symptoms.

Sequential pattern profiling based bio-detection for smart health service

Due to the development of IT convergence technologies, increased attention has focused on smart health service platforms to detect emergency situations related to chronic disease, telemedicine, silvercare, and wellness. Moreover, there is a high demand for technologies that can properly judge a situation and provide suitable countermeasures or health information if an emergency situation occurs. In this paper, we propose the sequential pattern analysis based bio-detection for smart health services. A smart health service platform is able to save bio-images and their locations detected in a smart health surveillance area where CCD cameras are installed. When a person’s figure is saved, the route tracing detects any movement and then traces its location. In addition, the platform analyzes the perceived bio-images and sequential patterns in order to determine whether or not the emergency situation is normal. Using AprioirAll algorithm-based sequential pattern profile analysis, bio-detection can detect a user who is undergoing an emergency based on abnormal patterns. It performs this task by managing information obtained from data and trace analyses, and it starts bio-detection only when there are patterns not conforming to sequential patterns. In other words, bio-detection detects the maximum sequence that can satisfy the minimum support in a given transaction. Sequential pattern profile analysis based on life-logs can analyze normal and abnormal profiles to provide health guidelines.

3D simulator for stability analysis of finite slope causing plane activity

This paper describes the development of a 3D simulator that enables a user to analyze the stability of a finite slope causing plane activity among a range of slopes comprised of a finite slope and infinite slope. Until now, there has been considerable theory and research into slope stability. Nevertheless, few systems can be confirmed directly by simulating the stability analysis of a slope, such as landslides. In other words, virtual experiments, such as the analysis of the slope, cannot be performed due to the absence of a system. For that reason, in this study, a 3D simulator was developed for stability analysis of a finite slope causing plane activity from the landslide phenomena that actually occurred or had very high probability. The Nvidia PhysX, which is utilized to develop computer games and simulators, was used to develop a 3D simulator with physical features. In addition, OpenGL was used to provide a three-dimensional visual effect from the simulator. In this paper, the values of each variable were determined to confirm whether landslides can occur easily when the factor of safety (Fs) was within a certain range in the 3D simulator. The 3D simulator developed in this paper was found to be quite useful because it can verify visually whether landslides occur easily in different environments and conditions.

Development of head detection and tracking systems for visual surveillance

This paper proposes a technique for the detection of head nod and shake gestures based on eye tracking and head motion decision. The eye tracking step is divided into face detection and eye location. Here, we apply a motion segmentation algorithm that examines differences in moving people’s faces. This system utilizes a Hidden Markov Model-based head detection module that carries out complete detection in the input images, followed by the eye tracking module that refines the search based on a candidate list provided by the preprocessing module. The novelty of this paper is derived from differences in real-time input images, preprocessing to remove noises (morphological operators and so on), detecting edge lines and restoration, finding the face area, and cutting the head candidate. Moreover, we adopt a K-means algorithm for finding the head region. Real-time eye tracking extracts the location of eyes from the detected face region and is performed at close to a pair of eyes. After eye tracking, the coordinates of the detected eyes are transformed into a normalized vector of x-coordinate and y-coordinate. Head nod and shake detector uses three hidden Markov models (HMMs). HMM representation of the head detection can estimate the underlying HMM states from a sequence of face images. Head nod and shake can be detected by three HMMs that are adapted by a directional vector. The directional vector represents the direction of the head movement. The vector is HMMs for determining neutral as well as head nod and shake. These techniques are implemented on images, and notable success is notified.

Context and profile based cascade classifier for efficient people detection and safety care system

This study propose a system of extracting and tracking objects for a multimedia system and addresses how to extract the head feature from an object area. It is observed in images taken from real-time records like a video, there is always a variance in human behavior, such as the position, size, etc. of the person being tracked or recorded. This study discusses how to extract and track multiple objects based on context as opposed to a single object. Via cascade extraction, the proposed system allows tracking of more than one human at a time. For this process, an extraction method based on internal and external contexts, which defines features to distinguish a human, is proposed. The proposed method defines shapes of shoulder and head area to recognize the head-shape of a human, and creates an extractor according to its edge information and geometrical shapes context. In this paper, humans in images are extracted and recognized using contexts and profiles. The proposed method is compared with a single face detector system and it shows better performance in terms of precision and speed. This trace information can be applied in safety care system. Extractions can be improved by validating the image using a context based detector when there are duplicated images.