• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.23-31
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• 2012

Nowadays, recognizing human activities is an important subject; it is exploited widely and applied to many fields in real-life, especially in health care and context aware application. Research achievements are mainly focused on activities of daily living which are useful for suggesting advises to health care applications. Falling event is one of the biggest risks to the health and well-being of the elderly especially in independent living because falling accidents may be caused from heart attack. Recognizing this activity still remains in difficult research area. Many systems equipped wearable sensors have been proposed but they are not useful if users forget to wear the clothes or lack ability to adapt themselves to mobile systems without specific wearable sensors. In this paper, we develop a novel method based on analyzing the change of acceleration, orientation when the fall occurs and measure their similarity to featured fall patterns. In this study, we recruit five volunteers in our experiment including various fall categories. The results are effective for recognizing fall activity. Our system is implemented on G1 smart phone which are already plugged accelerometer and orientation sensors. The popular phone is used to get data from accelerometer and results showthe feasibility of our method and significant contribution to fall detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1167-1174
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• 2009

In the monitoring of a patient in a sickroom, not only the physiologic and environmental data of the patient, which is automatically measured, but also the clinical data(clinical chart)of the patient, which is drew up by a doctor or nurse, are recognized as important data. However, since in the current environment of a sickroom, clinical data is collected being divided from the data that is automatically measured, the two data are used without an effective integration. This is because the integration of the two data is difficult due to their different collection times, which leads the reconstruction of clinical data to be remarkably uncertain. In order to solve these problems, a method to synchronize the continuous environmental data of a sickroom and clinical data is appearing as an important measure. In addition, the increase of use of small machines and the development of solutions based on wireless communications provide a communication platform to the developers of health care. Thus, this paper realizes a remote system for taking care of patients based on a web that uses mobile phones. That is, clinical data made by a nurse or doctor and the environmental data of a sick room comes to be collected by a collection module through a wireless sensor network. An observer can see clinical data and the environmental data of a sickroom through his/her mobile phone, integrating and storing his/her data into the database. Families of a patient can see clinical data made by hospital and the environment of the sick room of the patent through their computers or mobile phones outside the hospital. Through the system,hospital can provide better medical services to patients and their families.

• Journal of Digital Convergence
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• v.13 no.7
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• pp.137-145
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• 2015

Mobile's health care and diagnostic capabilities are accepted as the most innovative sectors, and the combination with wearable device is being predicted as the future's most promising industries. However, the level of consumer acceptance and utilization is still insufficient compared to the development of technology. This study explores the use intention of wearable device based on previous studies about health apps. In particular, health-related use efficacy of the app, app use intention and app satisfaction are analyzed through a structural equation model. The use of health-related apps revealed that there is a statistically significant relationship with the use of the wearable device. The results suggest that the importance of app use efficacy, satisfaction, apps use should be considered for the successful diffusion of wearable health devices.

• Nutrition Research and Practice
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• v.18 no.2
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• pp.294-307
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• 2024

BACKGROUND/OBJECTIVES: Dietitians frequently use nutrition education tools to facilitate dietary counselling sessions. Nevertheless, these tools may require adaptation to keep pace with technological advancements. This study had a 2-fold purpose: first, to identify the types of nutrition education tools currently in use, identify their limitations, and explore dietitians' perspectives on the importance of these tools; second, to investigate the features that dietitians prefer in digital nutrition education tools. SUBJECTS/METHODS: A semi-structured face-to-face interview was conducted among 15 dietitians from selected public hospitals, primary care clinics, and teaching hospitals in Malaysia. Inductive thematic analysis of the responses was conducted using NVivo version 12 software. RESULTS: Most dietitians used physical education tools including the healthy plate model, pamphlets, food models, and flip charts. These tools were perceived as important as they facilitate the nutrition assessment process, deliver nutrition intervention, and are time efficient. However, dietitians described the current educational tools as impersonal, outdated, limited in availability due to financial constraints, unhandy, and difficult to visualise. Alternatively, they strongly favoured digital education tools that provided instant feedback, utilised an automated system, included a local food database, were user-friendly, developed by experts in the field, and seamlessly integrated into the healthcare system. CONCLUSION: Presently, although dietitians have a preference for digital educational tools, they heavily rely on physical nutrition education tools due to their availability despite the perception that these tools are outdated, impersonal, and inconvenient. Transitioning to digital dietary education tools could potentially address these issues.

• Journal of Physiology & Pathology in Korean Medicine
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• v.27 no.6
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• pp.827-831
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• 2013

This study aims to develop a pattern identification system (PIS) for general users to check up their body condition. We collected previous 3 PI questionnaires, with internal consistency reliability or validity of the diagnosis by experts, through a field test. For defining weights of pattern indices, we applied the analytic hierarchy process (AHP) method with 11 experts. PIS receive two kinds of symptoms of users : body region based symptoms and core symptoms for PIS. PIS suggest possible patterns and health information on the basis of selected symptoms with analysis by AHP. This study showed PIS could be easily used for general user who wants to access Korean Medicine compared to conventional PI system. Furthermore, it could be utilized with mobile environment or as remote medicine care.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.2
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• pp.84-87
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• 2016

This paper deals with Energy Harvesting using a part of physical energy which is generated by pulldown actions. From a point of energy harvesting view, though it is difficult to regenerate constant energy during pulldown exercise unlike cyclic exercise and running machine exercise, it is possible to regenerate some energies to charge batteries for mobile phones. To investigate possibility of energy harvesting during pulldown exercise, a voltage regulation experiment was performed with a converter circuit and a motor which is attached to the exercise equipment. And we conclude that energy harvesting is possible although its small quantity during pulldown exercises.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.920-922
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• 2012

논문은 기존의 헬스케어 서비스의 한계를 극복하기 위해 최근 대두되고 있는 스마트 헬스케어서비스(smart healthcare service)에 관한 연구이다. 스마트 헬스케어는 스마트폰의 기능에 맞추어 기초체력 운동 지수, 섭취 칼로리, 심박 체크 등의 다양한 서비스를 제공할 수 있지만, 최근에는 보건의료서비스에 특화시켜 환자와 의사가 직 간접적으로 진료를 할 수 있게끔 하는 스마트폰 환경에서의 원격진료 서비스가 부각되고 있다. 이에 본 논문에서는 게이트웨이 기능의 스마트폰을 기반으로 스마트 헬스케어 시스템을 설계 구현하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.47.3-47
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• 2001

This paper describes the control of a health care robot (called Welfare Liferobot) with voice commands. The welfare liferobot is an intelligent autonomous mobile robot with its own control system on-board and the set of sensors to perceive an environment. It is a natural way to control the welfare liferobot by use of voice command for the usage of keyboard and mouse may present a difficult problem to the elderly and the handicapped. Voice input as the main control modality can offer many advantages. A set of oral commands is included, and each command has its associated function. These control words (commands) have to be chosen by user. Each time a voice command is recognized by the robot, it executes the pre-assigned action ...

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.29-35
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• 2021

While the population of pet dogs and pet-related markets are increasing, there is no convenient and reliable tool for pet health monitoring for pet owners/caregivers. In this paper, we propose a mobile platform-based pre-diagnosis system that pet owners can use for pre-diagnosis and obtaining information on coping strategies based on their observations of the pet dog's abnormal behavior. The proposed system constructs symptom-disease association databases for 100 frequently observed diseases under veterinarian guidance. Then, we apply the possibilistic fuzzy C-means algorithm to form the "probable disease" set and the "doubtable disease" set from the database. In the experiment, we found that the proposed system found almost all diseases correctly, with an average of 4.5 input symptoms and outputs 1.5 probable and one doubtable disease on average. The utility of this system is to alert the owner's attention to the pet dog's abnormal behavior and obtain an appropriate coping strategy before consult a veterinarian.