• Journal of Information Processing Systems
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• v.15 no.3
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• pp.478-491
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• 2019

This work develops a monitoring system for the population with health concerns. A belt integrated with an on-body circuit and sensors measures a wearer's selected vital signals. The electrocardiogram sensors monitor heart conditions and an accelerometer assesses the level of physical activity. Sensed signals are transmitted to the circuit module through digital yarns and are forwarded to a mobile device via Bluetooth. An interactive application, installed on the mobile device, is used to process the received signals and provide users with real-time feedback about their status. Persuasive functions are designed and implemented in the interactive application to encourage users' physical activity. Two signal processing algorithms are developed to analyze the data regarding heart and activity. A user study is conducted to evaluate the performance and usability of the developed system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.211-212
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• 2021

Recently, as interest in health increases, the wearable market that can collect various biometric information is expanding. In addition, telemedicine and healthcare services through these bio-signals are expected to become common. In this paper, we introduce a service that can store bio-signals collected through IoT equipment in a database and monitor them in real time through the web. By implementing a system for collecting and storing biometric data and real-time monitoring, it can be utilized for various health management diagnosis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.654-656
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• 2015

In today's world a monitoring system for managing obesity is very important in accordance with the trend of increasing obesity all through out the world. Most of the monitoring systems available today are quite basic and it simply calculate's only the BMI figures and the weight. Because it shows only basic information, this kind of system is not quite efficiently or effective. In this paper, our research concludes that there is a scop for a more efficient BMI system and we have designated a web-based monitoring system that recommends not just weight and BMI but also detailed graphs and recommends equipments in accordance with the users BMI level.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.18-26
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• 2009

In this paper, we proposed multipurpose signal detection methods for ECG (electrocardiogram) based u-healthcare systems. For ECG based u-healthcare system, QRS signal extraction for cardiovascular disease diagnosis is essential. Also, for security and convenience reasons, it is desirable if u-healthcare system support biometric identification directly from user's bio-signal such as ECG for this case. For this, from Lead II signal, we developed QRS signal detection method and also, we developed signal extraction method for biometric identification using Lead II signal which is relatively robust from signal alteration by aging and diseases. For QRS signal detection capability from Lead II signal, ECG signals from MIT-BIH database are used and it showed 99.36% of accuracy and 99.68% of sensitivity. Also, to show the performance of signal extraction capability for biometric diagnosis purpose, Lead III signals are measured after drinking, smoking, or exercise to consider various monitoring conditions and it showed 99.92% of accuracy and 99.97% of sensitivity.

• Science of Emotion and Sensibility
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• v.9 no.1
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• pp.77-84
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• 2006

In this study, there was emphasis in presenting a basic direction for the development of a healthcare smart clothing that could monitor diseases. It was also important that the clothing be user-friendly, for everyday life. For achieving this purpose , we studied major health indicators and essential technologies for developing healthcare smart clothing, and carried out the consumer research regarding healthcare smart clothing so it would appeal to consumers. As a result, there was a high demand for clothing that could diagnose diseases such as hypertension, diabetes and metabolic diseases, for all age groups. Thus, its marketability was predicted to be high. The results of this study will become an important index in developing future healthcare smart clothing.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.18-29
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• 2019

In the field of Human-Computer Interaction design, persuasive design has gradually been applied to the system development and design process, especially for mobile application design. However, most mobile applications have hitherto a very short using lifecycle. Especially, design features with long-term persuasive effectiveness remain to be further researched and developed. In this study, we focused on investigating and identifying the durative persuasive design characteristics through a data mining process and evaluating the durative effectiveness through a long-term observation process. Total five hundred healthcare-related mobile applications were selected from Apple iTunes Store and a mixed method was conducted to extract the most common persuasive design characteristics. Based on the results of extraction, a representative healthcare-related mobile application was selected as experimental subject. Total one hundred and twenty participants were observed during a six-months experiment and the monitoring data of app usage of all participants was collected once a week. According to the evaluation model for behavior change identification process, participants with habit formation features were proved to have a significant long-term perception level for ten persuasive design characteristics. Further interview research was performed to investigate the participant's long-term perceptions on those characteristics for the purpose of identifying the durative persuasions. The results indicated that a long-term durative effectiveness can be observed and healthcare-related apps designed with those characteristics could have durative effectiveness. This study may contribute to the improvement of future mobile application designs in user experience and durative persuasion, as well as bringing future benefits for both mobile application developers and users.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.197-204
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• 2015

Recent changes in health care environment including aging population and prevalence of chronic disease encourage the adoption of new innovative technological solutions including wearable vital sensors, wireless networks, and smart phone. In this paper, we present an effective at-home lifestyle monitoring system that can be used for self-management and health intervention of patient himself in the Management-by-Exception perspectives. We implemented the filtering and queuing algorithms as a preprocessor of monitoring system to enhance efficiency of proposed system, and the effective UX design for self-management of patients themselves. The 94,467 actual clinic data was used to test the efficiency of the proposed system. As as a result, 64.8% of the incoming vital data was identified to be filtered out.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.8
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• pp.253-258
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• 2015

From the point of view of u-healthcare, heart rate is so useful for both illness for taking care of patients and wellness for improving the level of health and wellbeing. It is because heart rate is a significant clinical variable for all kinds of diseases as well as an indicator of the intensity of exercise. Recently, a number of various wearable heart rate monitors have been released to check people's status in the body by monitoring their heart rates. In addition, a number of smartphone applications have been released to conveniently monitor the status of exercise by using heart rate monitors. However, all of these applications are limited to a personal usage. In this paper, we will design a system to simultaneously monitor heart rates coming from multiple users in a real-time, and develop an Android application to apply the system. The application mainly features a simultaneous monitoring of heart rates coming from multiple users, allowing to be effectively applied to fitness centers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2349-2354
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• 2006

Due to technological developments and the joint effect of both new social and economic needs and constraints, telemedicine is expanding rapidly through a variety of applications. Especially, owing to the rapid aging of society and increasing the wish for well being life, we take interest in health care services for people with special needs who wish to remain independent and living in their own home. We have focused on tole-monitoring to real-time medical signal and environment factor which is an influence on medical signal. We monitor the six signal(medical signal and environment factor), and transmit that signal to computer on bluetooth network. We get the information after using the some digital signal processing system, and display that information on the real-time monitoring system. We developed the measurer as portable type in older to non-restrained monitor.

• Journal of Biomedical Engineering Research
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• v.35 no.4
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• pp.87-94
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• 2014

Recent technological advances have increased interest in personal health monitoring. Electrocardiogram(ECG) monitoring is a basic healthcare activity and can provide decisive information regarding cardiovascular system status. In this study, we developed a capacitive ECG measurement system that can be included within a cloth mattress pad. The device permits ECG data to be obtained during sleep by using capacitive electrodes. However, it is difficult to detect R-wave peaks automatically because signals obtained from the system can include a high level of noise from various sources. Because R-peak detection is important in ECG applications, we developed an algorithm that can reduce noise and improve detection accuracy under noisy conditions. Algorithm reliability was evaluated by determining its sensitivity(Se), positive predictivity(+P), and error rate(Er) by using data from the MIT-BIH Polysomnographic Database and from our capacitive ECG system. The results showed that Se = 99.75%, +P = 99.77%, and Er = 0.47% for MIT-BIH Polysomnographic Database while Se = 96.47%, +P = 99.32%, and Er = 4.34% for our capacitive ECG system. Based on those results, we conclude that our R-peak detection method is capable of providing useful ECG information, even under noisy signal conditions.