• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.791-792
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• 2011

폐쇄성 수면 무호흡증 환자 중 가장 위험한 2~8세 아동들의 지속적인 수면 상태를 모니터링하는 것은 중요하며, 야간에 수면 상태를 모니터링 하는 것은 보호자에게 스트레스와 일상생활에 지장을 초래할 수 있다. 본 연구에서는 아동들의 수면 상태를 측정하기 위한 스마트폰 기반의 실시간 호흡 모니터링 시스템을 구현하였다. 구현된 시스템은 아동의 수면 중 자세 변화로 인한 잦은 폐쇄성 수면 무호흡을 지속적으로 모니터링 함으로써 수면 상태 모니터링 및 위험 상황을 인지할 수 있으며, 보호자에게 편안한 수면을 제공한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.153-159
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• 2019

Respiration is one of the important vital signs to determine the condition of the patient. Especially during deep sedation, since the patient's apnea and hypopnea are difficult to detect without continuous monitoring, there is a need for a continuous respiration monitoring method that can accurately and simply determine the patient's respiratory condition. Currently, respiration monitoring methods using various devices have been developed, but these methods have not only late response time but also low reliability at the clinical stage. In this study, attachable sensor using PVDF(polyvinylidene fluoride) film and a monitoring device which could detect abnormal symptoms of breathing in early stage during deep sedation. The results of this study can be used in various medical fields including not only in the area of remote monitoring for respiration related sleep monitoring but also in routine monitoring during deep sedation.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.491-498
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• 2022

In our daily life, quality of sleeping is closely related to happiness index. Whether or not people perceive sleep disturbance as a chronic disease, people complain of many difficulties, and in their daily life, they often experience difficulty breathing during sleep. It is very important to automatically recognize breathing-related disorders during a sleep, but it is very difficult in reality. To solve this problem, this paper proposes a mobile-based non-contact sleeping monitoring for health management at home. Respiratory signals during the sleep are collected by using the sound sensor of the smartphone, the characteristics of the signals are extracted, and the frequency, amplitude, respiration rate, and pattern of respiration are analyzed. Although mobile health does not solve all problems, it aims at early detection and continuous management of individual health conditions, and shows the possibility of monitoring physiological data such as respiration during the sleep without additional sensors with a smartphone in the bedroom of an ordinary home.

• Science of Emotion and Sensibility
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• v.22 no.1
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• pp.89-100
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• 2019

Sleep apnea, a medical condition associated with a variety of complications, is generally monitored by standard sleep polysomnography, which is expensive and uncomfortable. To overcome these limitations, this study proposes an unconstrained wearable monitoring system with stretch-fiber sensors that integrate with the wearer's clothing. The system allows patients to undergo examinations in a familiar environment while minimizing the occurrence of skin allergies caused by adhesive tools. As smart clothing for adult males with sleep apnea, long-sleeved T-shirts embedding fibrous sensors were developed, enabling real-time monitoring of the patients' breathing rate, oxygen saturation, and airflow as sleep apnea diagnostic indicators. The gauge factor was measured as 20.3 in sample 4. The maximum breathing intake, measured during three large breaths, was 2048 ml. the oxygen saturation was measured before and during breath-holding. The oxygen saturation change was 69.45%, showing a minimum measurable oxygen saturation of 70%. After washing the garment, the gauge factor reduced only to 18.0, confirming the durability of the proposed system. The wearable sleep apnea monitoring smart clothes are readily available in the home and can measure three indicators of sleep apnea: respiration rate, breathing flow and oxygen saturation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.1004-1005
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• 2013

본 연구에서는 폐쇄성 수면 무호흡증 환자를 대상으로 착용의 불편함을 최소화하고 수면 중 지속적인 모니터링이 가능한 안대형 폐쇄성 수면 무호흡증 검출시스템을 구현하였다. 이를 위하여 숙면을 돕기 위해서 착용하는 안대의 코 부근에 온도센서를 부착하여 실제 호흡에 따른 온도 변화를 감지하였다. 폐쇄성 수면 무호흡증 환자의 경우 수면 중 불안정한 호흡이 온도의 변화로 반영되기 때문에 이를 검출하기 위함이다. 또한 검출된 온도 변화는 안대에 내장된 제어부 및 블루투스 모듈을 통해 스마트폰으로 전송되어진다. 전송된 데이터는 안드로이드 기반의 어플리케이션을 구현하여 실시간으로 모니터링이 가능하며, 구현된 어플리케이션은 위험상황 인지 및 알림, 일월별 관리 기능을 포함하고 있다. 구현된 시스템의 성능 평가를 위하여 대학생 5명을 대상으로 임의의 호흡 변화에 대한 실험 프로토콜을 작성하여 실험을 수행하였으며, 그 결과 호흡을 중단하였을 시 온도 변화를 통해 검출이 가능함을 확인하였다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1423-1424
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• 2015

본 연구는 IoT 환경에서 수면 중 비정상호흡 모니터링을 위한 모듈개발 사전연구로, 베개 안에 삽입할 수 있는 가속도, 진동 측정 모듈을 제작하고, 측정된 신호를 기반으로 수면 중 발생하는 호흡활동을 관찰하는 것을 목적으로 한다. 이를 위하여 압전센서 및 3축 가속도 센서를 내장한 진동, 가속도 측정 모듈 프로토타입을 설계 및 제작하였으며, 파일럿 실험을 통하여 개발된 모듈의 동작을 확인하였다. 실험 결과 가속도 및 압전센서에서 획득된 신호에서 호흡성분이 검출되는 것을 확인하였으나, 샘플링율, 센서 민감도 설정에 따라 코골이 성분은 검출되지 않았다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.7-13
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• 2020

This paper focuses on detecting abnormal patterns of respiration of humans. In this study, a contact-based device was used to acquire both normal and abnormal respiration signals. To this end, this paper reports the development of a monitoring system to investigate the respiratory status of humans in a normal environment. This work aims to classify the respiratory status, i.e., normal and abnormal status, quantitatively. The respiration signal is acquired using a contact-based medical device (BIOBPAC), and noise reduction is carried out before classifying the respiratory status. To reduce noise, a mixed filter that combines the Savitzky-Golay filter and Median filter is applied to the acquired respiration signals. The inter-class distance is maximized, and the intra-class distance is minimized. The proposed algorithm is straightforward and can be applied to a practical environment. In addition, the experimental results are provided to substantiate the proposed approach.

• Journal of Biomedical Engineering Research
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• v.23 no.6
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• pp.485-489
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• 2002

In this study. respiratory efforts were monitored by the change of pulse transit time (PTT) which is related with the arterial pressure PTT is the time interval between the peak of R wave in ECG and the maximal slope point of photoplethysmogram(PPG). Biosignals, ECG and finger photoplethysmogram(PPG), were converted to digital data, and PTT was evaluated in personal computer with every heart beat. Results were presented as a graph using spline interpolation. The software was implemented in C$\^$++/ as a window-based application program. PTT was periodically changed according to airflow in resting respiration. In the resting respiration, PTT was changed according to the respiratory cycle. The amplitude of PTT fluctuation was increased by deep respiration, and increased by partial airway obstruction. These results suggest that PTT is responsible to respiratory effort which could be evaluated by the pattern of PTT change. And it is expected that PTT could be applied in the monitoring of respiratory effort by noninvasive methods, and is very useful method for the evaluation of respiratory distress.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.561-563
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• 2015

본 논문은 깊이 카메라를 이용하여 수면중에 가슴의 움직임만으로 호흡률을 예측하는 시스템을 제안한다. 카메라는 취침하는 사람의 머리 위에 위치하였으며, 취침하는 사람의 가슴 주변을 관심 영역으로 지칭하여 깊이 값의 변화를 추출하여 노이즈를 제거한 후 FFT를 계산하여 호흡률을 계산하였다. 실행에서 10명의 지원자를 대상으로 0.1 Hz 부터 0.4 Hz까지 측정하여 약 98%의 정확률을 얻었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.119-129
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• 2023

Sleep apnea causes various secondary disease such as hypertension, stroke, myocardial infarction, depression and cognitive impairment. Early detection and continuous management of sleep apnea are urgently needed since it causes cardio-cerebrovascular diseases. In this study, wearable device for monitoring respiration during sleep using PVDF film was developed to detect vibration through trachea caused by breathing, which determines normal breathing and sleep apnea. Variables such as respiration rate and apnea were extracted based on the detected breathing sound data, and a noise reduction algorithm was established to minimize the effect even when there is a noise signal. In addition, it was confirmed that irregular breathing patterns can be analyzed by establishing a moving threshold algorithm. The results show that the accuracy of the respiratory rate from the developed device was 98.7% comparing with the polysomnogrphy result. Accuracy of detection for sleep apnea event was 92.6% and that of the sleep apnea duration was 94.0%. The results of this study will be of great help to the management of sleep disorders and confirmation of treatment by commercialization of wearable devices that can monitor sleep information easily and accurately at home during daily life and confirm the progress of treatment.