• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.27-33
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• 2022

Sleep is an essential factor for living a healthy life, but most modern people complain of poor sleep. For these people, as the need for a means to simply evaluate and manage the quality of sleep increases, devices that can check the sleep state at home without monitoring by an examiner are being developed. The smart sleep mask, which is the subject of this usability test, provides bio-signal monitoring while sleeping so that you can conveniently measure and manage your sleep state for yourself. The purpose of this study is to evaluate the usability and safety of the smart sleep mask, to find and prevent potential factors related to errors in use that may occur, and to develop the comfort and safety of this product. As a result of the formative evaluation of the sleep mask prototype, it was reported that it was difficult to turn on the power and check the results, and that the sleep mask was not comfortable to wear. Different opinions were presented on the size and weight of the sleeping mask by people in different age groups.

• Journal of Biomedical Engineering Research
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• v.37 no.4
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• pp.127-133
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• 2016

This study proposes the feasibility for automatic classification of sleep/wakefulness using nasal pressure in patients with sleep-disordered breathing (SDB). First, SDB events were detected using the methods developed in our previous studies. In epochs for normal breathing, we extracted the features for classifying sleep/wakefulness based on time-domain, frequency-domain and non-linear analysis. And then, we conducted the independent two-sample t-test and calculated Mahalanobis distance (MD) between the two categories. As a results, $SD_{LEN}$ (MD = 0.84, p < 0.01), $P_{HF}$ (MD = 0.81, p < 0.01), $SD_{AMP}$ (MD = 0.76, p = 0.031) and $MEAN_{AMP}$ (MD = 0.75, p = 0.027) were selected as optimal feature. We classified sleep/wakefulness based on support vector machine (SVM). The classification results showed mean of sensitivity (Sen.), specificity (Spc.) and accuracy (Acc.) of 60.5%, 89.0% and 84.8% respectively. This method showed the possibilities to automatically classify sleep/wakefulness only using nasal pressure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.722-727
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• 2009

The technology of distinguishing human motion states is required in the areas of measuring and analyzing biosignals changing according to physical activities, diagnosing sleep disorder, screening the effect of treatment, examining chronic patients' kinetic state, prescribing exercise therapy, etc. The present study implemented a fuzzy inference system based on fuzzy rules that distinguish human motion states (tying, sitting, walking, and running) by acquiring and processing data of LAA, TAA, L-MAD, and T-MAD using ADXL202AE of Analog Devices embedded in an armband. The membership degree and fuzzy rules in each area of input (LAA, TAA, L-MAD, and T-MAD) and output (tying, sitting, walking, and running) data used here were determined using numeric data obtained from experiment. In the results of analyzing data for simulation generated in order of tying$\rightarrow$walking$\rightarrow$running$\rightarrow$tying, the sorting rate for motion states tying, sitting, walking, and running was 100% for each motion.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.5-10
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• 2004

Body movement is one of the important factors in sleep analysis. In this study, a thermopile detector with four channels was implemented as a non-contacting detector of body movement in sleep. Using a thermopile mathematical model and several frames of thermal images, the possibility of detecting body movement was evaluated. Instant body movement signals were evaluated for the upper, lower, and entire body using the Haar wavelet. This decomposition shows the points in time when the upper-body or lower-body movement occurred and the level of body movement. Additionally, partial body movement was decomposed in head-only, whole body, and leg-only movement using the ANFIS algorithm. Finally, three subject's data were evaluated for 60 minutes, and the detection rates of instant and partial body movement, on average, were 96.3％ and 89.2％, respectively.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.609-616
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• 2018

In this paper, we designed a multimodal bio-signal measurement system to observe changes in the brain nervous system and vascular system during sleep. Changes in the nervous system and the cerebral blood flow system in the brain during sleep induce a unique correlation between the changes in the nervous system and the blood flow system. Therefore, it is necessary to simultaneously observe changes in the brain nervous system and changes in the blood flow system to observe the sleep state. To measure the change of the nervous system, EEG, EOG and EMG signal used for the sleep stage analysis were designed. We designed a system for measuring cerebral blood flow changes using functional near-infrared spectroscopy. Among the various imaging methods to measure blood flow and metabolism, it is easy to measure simultaneously with EEG signal and it can be easily designed for miniaturization of equipment. The sleep stage was analyzed by the measured data, and the change of the cerebral blood flow was confirmed by the change of the sleep stage.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.459-463
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• 1997

In this paper, we present the elimination of ECG artifact from the polysomnographic EEG and EOG. The idea of this method is that the ECG synchronized EEG segment is detected from ECG and regard samples of that segment a missing signal. After this, we used two interpolation methods to recover the missing segment. One is the Lagrange Polynomial Interpolation Method and the other is the Least Square Error AR Interpolation method. We tested those methods by applying to simulated signals. AR methods works well enough to reject the artifact about 10% of the main artifact level. We practically applied to real EEG and EOG signals. We also developed the algorithm to detect whether the artifact level is high or not. If the artifact level is high, then the interpolations are applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.660-662
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• 2022

Due to the prolonged aftermath of COVID-19, the number of modern people suffering from stress and mental illness is increasing. Recently, various methods of color therapy are being studied using LED lighting to improve concentration, relieve stress, manage skin, and improve sleep quality. In this paper, pulse waves are measured and heart rate variability is extracted using a PPG (Photoplethysmogram) sensor to analyze a person's mental and physical state. Using RGBLED and Arduino, we propose a mood lighting system that automatically changes colors according to changes in mental and physical conditions by producing color lighting of various wavelengths, which are mainly used to stabilize the mental state.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.671-677
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• 2012

A black-box program was implemented in order to monitor abnormal symptoms of human body irregularly occurring during sleep. The system consists of sensor probing body signals, auxiliary devices such as the alarm, lamp, network camera, and signal monitoring computer. Various types of sensors, PPG, ECG, EEG, temperature, respiration sensor, G-sensor, and microphone were used to more exactly identify the causes of abnormal symptoms. If a symptom occurs, the system records the patient's condition to provide information being utilized in the treatment. The sensors are attached on some locations of body being proper to check a specific type of abnormal reaction. Based on the normal range and type of measurement data, criteria of signal levels were set to distinguish abnormal reaction. An abnormal signal being probed, the program starts to operate the lamp, alarm, and network camera at the same time and stores the signal and video data.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.63-66
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• 2009

본 연구의 목표는 사용자에게 적합한 매트리스를 선택하는데 도움을 주는 신체적, 심리적 지표를 개발하고, 이를 분석하여 개인의 주관적 만족도와의 상관성을 알아보는 것이다. 시중에 판매되고 있는 5개의 매트리스의 강도를 2개씩 짝지어진 매트리스별 pared t-test를 통하여 강도별 순위를 5등급으로 분류하였다. 10명의 실험 참가자들의 신체적 지표(근전도, 심박수, 산소포화도 측정), 심리적 지표(개인 특성 설문 조사), 체압 및 주관적 만족도 측정을 실시하고, 상관분석을 통해서 이 측정결과와 개인간 주관적 만족도(선호하는 침대의 탄성)와도 일관성이 있는지 조사하였다. 연구 결과, 압력집중도(r=0.818)과 허리근육이완도(r=0.766) 그리고 심박수(r=0.670)가 주관적 만족도와 유의한 상관관계를 가지는 것으로 나타났다.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.166-166
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• 2017

모돈의 건강 상태를 정량 지수화 하기 위한 연구를 수행 중이다. 지제이상, 섭식 불량, 수면 패턴 등의 운동 특성 분석을 위하여 복수의 초음파 센서를 이용하였다. 시계열 계측 신호를 분석하여 정량 지수화를 수행하는 과정에서 주파수 도메인 분석을 시도하였다. 이 과정에서 주파수 도메인의 분해능에 따른 편차 극복을 위한 비선형 모델링을 수행하였다. 또한 인접한 시계열 데이터 구간 간의 상관성 분석이 가능하면 대용량 데이터의 실시간 처리로 인한 지연 시간 극복 및 기대되는 예후에 대한 조기 진단이 가능할 것이다. 본 연구에서는 구글에서 제공하는 Tensorflow와 NVIDIA에서 제공하는 CUDA 엔진을 동시 적용한 심층 학습 시스템을 이용하였다. 전 처리를 위하여 주파수 분해능 (2분, 3분, 5분, 7분, 11분, 13분, 17분, 19분)에 따른 데이터 집합을 1단계로 두고, 상위 10 순위 안에 드는 파워 스펙트럼 밀도의 크기를 2단계로 하여, 총 2~10개의 입력 노드를 순차적으로 선정하였고, 동일한 방식으로 인접한 시계열의 파워 스펙터럼 밀도를 순위를 변화시켜 지정하였다. 대표적인 심층학습 모델인 Softmax regression with a multilayer convolutional network를 이용하여 Recursive feature selection 경우의 수를 $8{\times}9{\times}9$로 총 648 가지 선정하고, Epoch는 10,000회로 지정하였다. Calibration 모델링의 경우 Cost function이 10% 이하인 경우 해당 경우의 학습을 중단하였으며, 모델 간 상호 교차 검증을 수행하기 위하여 $_8C_2{\times}_8C_2{\times}_8C_2$ 경우의 수에 대한 Verification test를 수행하였다. Calibration 과정 상 모든 경우에 대하여 10% 이하의 Cost function 값을 보였으나, 검증 테스트 과정에서 모든 경우에 대하여 $r^2$ < 0.5 인 결정 계수 값이 나타났다. 단적으로 심층학습 모델의 과도한 적합(Over fitting) 방식의 한계를 보인 것이라 판단할 수 있다. 적합한 Feature selection 및 심층 학습 모델에 대한 지속적이고 추가적인 고려를 통해 과도적합을 해소함과 동시에 실효적이고 활용 가능한 Classification을 위한 입, 출력 노드 단의 전후 Indexing, Quantization에 대한 고려가 필요할 것이다. 이를 통해 모돈 생체 정보 정량화를 위한 지능형 현장 진단 기술 연구를 지속할 것이다.