• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• 대한의용생체공학회:의공학회지
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• 제26권5호
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• pp.343-349
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• 2005

Continuously motivating people to exercise regularly is more important than finding a way out of barriers such as lack of time, cost of equipment, lack of nearby facilities, and poor weather. Our proposed system presents practicable methods of motivation through a diverse exercise aid system. The Health Improvement and Management System (all-in-one system which saves space and maintenance costs) measures and evaluates a diverse body shape analysis and physical fitness test and directs users to automated personalized exercise prescription which is prescribed by the expert system of three types of exercise templates: aerobics, anaerobics, and leisure sports. Automated personalized exercise prescriptions are built into XML based documents because the data needs to be in the form of flexible, expansible, and convertible structures in order to process various exercise templates, BIOFIT, a digital exercise trainer, monitors and provides feedback on the physiological parameters while users are working out in the gymnasium. If these parameters do not range within the prescribed target zone, the device will alarm users to control the exercise and make the exercise trainer adjust systemically the proper exercise level. Numeric health information such as the report of the physical fitness test and the exercise prescription makes people stay interested in exercising. In addition, this service can be delivered through the Internet.

• 대한한방내과학회지
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• 제45권1호
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• pp.1-10
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• 2024

Objectives: This study investigated the pattern identification (PI) and clinical index of Parkinson's disease (PD) for personalized diagnosis and treatment. Methods: This prospective observational multi-center study recruited 100 patients diagnosed with PD from two Korean medicine hospitals. To cluster new subtypes of PD, items on a PI questionnaire (heat and cold, deficiency and excess, visceral PI) were evaluated along with pulse and tongue analysis. Gait analysis was performed and blood and feces molecular signature changes were assessed to explore biomarkers for new subtypes. In addition, unified PD rating scale II and III scores and the European quality of life 5-dimension questionnaire were assessed. Results: The clinical index obtained in this study analyzed the frequency statistics and hierarchical clustering analysis to classify new subtypes based on PI. Moreover, the biomarkers and current status of herbal medicine treatment were analyzed using the new subtypes. The results provide comprehensive data to investigate new subtypes and subtype-based biomarkers for the personalized diagnosis and treatment of PD patients. Ethical approval was obtained from the medical ethics committees of the two Korean medicine hospitals. All amendments to the research protocol were submitted and approved. Conclusions: An objective and standardized diagnostic tool is needed for the personalized treatment of PD by traditional Korean medicine. Therefore, we developed a clinical index as the basis for the PI clinical evaluation of PD. Trial Registration: This trial is registered with the Clinical Research Information Service (CRIS) (KCT0008677)

• 한국산학기술학회논문지
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• 제19권11호
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• pp.730-737
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• 2018

행복한 삶의 질을 목적으로 하는 의료소비자가 증가하면서 웹에 분산되어 있는 블로그의 의료 정보를 바탕으로 신뢰성 있는 의료 시설을 선택하고 고품질의 의료 서비스를 받음으로서, 시간과 비용을 절약할 수 있는 O2O 의료 마케팅 시장이 활성화 되고 있다. 인터넷, 모바일, SNS 등에서 증가하는 비정형 텍스트 데이터는 전문 의료 지식 이외에 작성자의 관심, 선호, 예상 등을 직간접적으로 반영하고 있기 때문에 의료정보의 신뢰성을 담보하기 어렵다. 본 연구에서는 빅데이터 및 MLP를 사용하여 의료정보 블로그를 분류 (의료블로그, 광고블로그)함으로서 사용자에게 보다 고품질의 의료정보 서비스를 제공하는 블로그 판단 시스템을 제안한다. 제안된 빅데이터 및 머신러닝 기술을 통해 인터넷상에 존재하는 국내의 다수 의료정보 블로그를 종합, 분석한 후 질환별 개인 맞춤형 건강정보 추천 시스템을 개발한다. 이를 통하여 사용자는 자신의 건강문제를 지속적으로 점검하고 가장 적절한 조치를 취함으로서 자신의 건강 상태를 유지하는 것이 가능할 것으로 기대된다.

• 전자공학회논문지
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• 제51권6호
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• pp.162-168
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• 2014

PHR(Personal Health Records : 개인평생건강기록) 은 진료정보를 제공하는 것을 기본서비스로 하며, 환자와 서비스 제공자에게 유용한 서비스를 제공해야 한다. 본 연구는 진료정보의 제공 외 사용자에게 적합한 서비스를 발굴하는데 목적이 있다. 서비스 대상은 암환자로 한정하였고 PHR 서비스 발굴을 위하여 기존 사례 분석, 의료진 인터뷰, 환자 설문을 진행하였다. 그 결과로 발굴된 서비스는 3가지이다. 첫째, 환자에게 치료계획(patient's clinical pathway)을 제공하고 두 번째 온라인 질의응답기능 제공, 세번째 환자 본인의 상태를 입력하는 기능 제공이다. 발굴된 명지 PHR 서비스는 웹과 앱(안드로이드)으로 약 3개월간 파일롯 테스트를 하였고 유용성을 확인하였다. 향후 상업화된 PHR 서비스가 되기 위해서는 치료계획의 등록을 위한 표준화 및 사용자 편의성을 고려한 모델이 되어야 할 것이다.

• 한국멀티미디어학회논문지
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• 제18권6호
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• pp.772-779
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• 2015

Advancing information technology have rapidly changed our service environment of life, culture, and industry. Computer information communication system is applied in medical, health, distribution, and business transaction. Smart is using new information by combining ability of computer and information. Although agriculture is labor intensive industry that requires a lot of hands, agriculture is becoming knowledge-based industry today. In agriculture field, computer communication system is applied on facilities farming and machinery Agricultural. In this paper, we designed and implemented application that provides personalized agriculture related information at the actual farming field. Also, this provides farmer a system that they can directly auction or sell their produced crops. We designed and implemented a system that parsing information of each seasonal, weather condition, market price, region based, crop, and disease and insects through individual setup on ubiquitous environment using location-based sensor network and processing data.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.7-10
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• 2023

This study proposes an AI-based shoe recommendation model based on user clothing image data to solve the problem of the global fashion industry, which is worsening due to factors such as the economic downturn. Shoes are an important part of modern fashion, and this research aims to improve user satisfaction and contribute to economic growth through a generative AI-based shoe recommendation service. By utilizing generative AI in the personalized consumer market, we show the feasibility, efficiency, and improvements through an accessible web-based implementation. In conclusion, this study provides insights to help fulfill consumer needs in the ever-changing fashion market by implementing a generative AI-based shoe recommendation model.

• 한국응용과학기술학회지
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• 제38권6호
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• pp.1433-1444
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• 2021

본 연구는 코로나 시대이후로 가속화된 뷰티분야의 4차 산업들과 융합으로 산업에서 요구되는 실무능력을 교육하기 위한 교육과정 개발을 목적으로 진행되었다. 여러 문헌의 탐색적 조사와 전문가의견을 수렴한 결과 뷰티산업은 앞으로 단일 아이템 또는 서비스 제공이 아닌 의료, 바이오, ICT, 인공지능 기술 등이 결합된 개인맞춤형 서비스 제공 산업으로 분야가 확장될 것으로 분석되었다. 분석 내용을 바탕으로 전통적인 뷰티산업에서 요구하는 기본 직무 능력 외에 갖추어야 할 디지털 활용능력을 추가하여 교육과정을 구성하고 과목을 도출하였다. 코로나 이후의 시대는 4차 산업혁명을 기반으로 다양한 산업의 변화를 가져올 것이며 이러한 변화에 대응하여 뷰티산업의 발전과 지속가능성을 위한 인재 개발을 위해 대학에서는 산업의 변화에 항상 주목해야 할 것이다.

• 한국컴퓨터정보학회논문지
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• 제15권1호
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• pp.23-30
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• 2010

의료 분야의 정보화와 다양화로 인해 한의학 분야에서도 지능화된 서비스를 제공해주는 온톨로지 기반의 지능형 의료 시스템에 관한 연구가 진행되고 있다. 지능형 의료 시스템은 온톨로지를 이용하여 복잡한 의료지식 및 개인의 의료정보등을구조화함으로써진단을과학화시키고보다나은의료서비스를제공하게해준다. 본 논문에서는 온톨로지를 이용하여 기본적인 의학 데이터, 진단 시 발생되는 임상데이터, 개인의 신체정보와 같은 세 가지 지식을 표현하여 온톨로지로 구축함으로써 개인 맞춤형 진단을 내리는데 중요한 데이터로 활용한다. 특히, 한의학진단에서는 환자 개인의 병증과 체질 등에 따라 상이한 진단 및 처방이 내려질 수 있기 때문에 개개인의 신체정보 및 질병 정보를 이용하여 사용자의 상황에 맞는 맞춤형의 진단 및 처방 서비스를 제공 해주는 지능형 진단보조시스템이 유용하다. 따라서 본 논문에서는 환자 개개인에게 맞춤형의 진단 서비스를 제공하기 위한 방법으로 개인의 신체정보 및 질병정보를 이용하여 한의학 온톨로지를 구축하고, 추론을 통해 진단을 내리는 한의학 진단보조시스템을 구현하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.1321-1322
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• 2012

With the advance of Information Technology (IT) and dynamic requirements, diverse application services have been provided for end users. With huge volume of these services and information, users are required to acquire customized services that provide personalized information and decision at particular extent of time. The case is more appealing in healthcare, where patients wish to have access to their medical record where they have control and provided with recommendation on the medical information. PHR (Personal Health Record) is most prevailing initiative that gives secure access on patient record at anytime and anywhere. PHR should also incorporate decision support to help patients in self-management of their diseases. Available PHR system incorporates basic recommendations based on patient routine data. We have proposed decision support service called "Smart CDSS" that provides recommendations on PHR data for diabetic patients. Smart CDSS follows HL7 vMR (Virtual Medical Record) to help in integration with diverse application including PHR. PHR shares patient data with Smart CDSS through standard interfaces that pass through Adaptability Engine (AE). AE transforms the PHR CCR/CCD (Continuity of Care Record/Document) into standard HL7 vMR format. Smart CDSS produces recommendation on PHR datasets based on diabetic knowledge base represented in shareable HL7 Arden Syntax format. The Smart CDSS service is deployed on public cloud over MS Azure environment and PHR is maintaining on private cloud. The system has been evaluated for recommendation for 100 diabetic patients from Saint's Mary Hospital. The recommendations were compared with physicians' guidelines which complement the self-management of the patient.