• 대한임베디드공학회논문지
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• 제14권3호
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• pp.123-131
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• 2019

In recent years, a non-contact respiration and heart rates monitoring via IR-UWB radar has been paid much attention to in various applications - patient monitoring, occupancy detection, survivor exploring in disaster area, etc. In this paper, we address a novel approach of real time heart rate estimation using IR-UWB radar. We apply sine fitting and peak detection method for estimating respiration rate and heart rate, respectively. We also deploy two techniques to mitigate the error caused by wrong estimation of respiration rate: a moving average filter and finding the frequency of the highest occurrence. Experimental results show that the algorithm can estimate heart rate in real time when respiration rate is presumed to be estimated accurately.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권4호
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• pp.167-174
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• 2002

This study describes a fetal heart rate(FHR) estimation algorithm using phonogram. Using a phonogram amplifier, various fetal heart sounds are collected in a university hospital. The FHR estimation algorithms consists of a lowpass filter, decimation, envelop detection, pitch detection, and post-processing. The post-processing is the FHR decision procedure using all informations of fetal heart rates. Using the algorithm and other parameters of fetal heart sound, a fetal monitoring software was developed. This can display the original signals, the FFT spectra, FHR and its trajectory. Even though the fetal phonogram amplifier detects the fetal heart sounds well, the sound quality is not so good as the ultrasonography. In case of very week fetal heart sound, autocorrelation of it showed clear periodicity. But two main peaks in one period is an obstacle in pitch detection and peaks are not so vivid. The proposed FHR estimation algorithm showed very accurate and stable results. Since the developed software displays multiple parameters in real time and has convenient functions, it will be useful for the phonogram-style fetal monitoring device.

• 전자공학회논문지
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• 제52권8호
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• pp.18-26
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• 2015

올바른 운동량 조절을 위해선 운동중의 심박수 측정이 중요하다. 최근 스마트 디바이스가 활발하게 사용됨에 따라, 운동중의 실시간 심박수 측정에 대한 관심이 급격하게 증가하고 있다. 고강도 운동 중에는 동잡음으로 인하여 손목 밴드 유형의 광혈류 (PPG : photoplethysmography) 측정기 신호로부터 정확한 심박수를 추정하는 것이 매우 어렵다. 본 논문에서는 손목밴드 유형의 광혈류 측정기 신호로부터 정확한 심박수 추정을 위한 효율적인 알고리즘을 제안하였다. 12개의 데이터 세트에 대하여 제안하는 알고리즘을 적용한 결과, 1.38의 분당심박수(BPM) 평균 절대 오차를 기록하였고, 0.9922의 추정 심박수와 실제 심박수간의 Pearson 상관계수를 얻었다. 제안하는 알고리즘은 웨어러블 디바이스에 적합한 빠른 연산속도와 정확한 추정을 가능케 한다.

• 한국컴퓨터정보학회논문지
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• 제28권12호
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• pp.1-7
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• 2023

딥러닝의 발전은 의료 분야에서도 다양한 응용을 가능하게 하고 있으며 이러한 애플리케이션 중에 심박수 측정은 개인의 건강을 관리하기 위한 필수적인 아이템이라 할 수 있다. 광혈류 측정을 이용한 기존 방법의 경우 스마트워치 같은 장비의 착용이 필수적이다. 그러나 최근 딥러닝 기술의 발전은 비침습식으로 원격에서 사용자의 얼굴 이미지를 분석하여 심박수를 높은 성능으로 측정가능하게 한다. 본 연구에서는 모바일 환경에서 사용 가능한 경량화된 심박수 추정 방법론을 제안한다. 이 방법론은 2D 컨볼루션에 기반한 특화된 2채널 네트워크 구조를 사용하여, 혈류와 근육 수축으로 인한 얼굴의 미세한 움직임과 색상 변화를 고려한다. 제안하는 네트워크 구조는 이미지 특성을 분석하는 인코더와 혈류량 파동을 예측하는 회귀 레이어로 구성되어있다. 이러한 복합적인 특성을 동시에 분석함으로써, 제한된 컴퓨팅 리소스를 가진 환경에서도 심박수를 정확하게 추정할 수 있다. 이 연구의 접근 방식은 침습적인 기술 없이도 심박수를 효과적으로 모니터링 할 수 있는 새로운 경로를 제공할 것으로 예상한다.

• 제어로봇시스템학회논문지
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• 제20권5호
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• pp.486-494
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• 2014

Intelligent homes consist of ubiquitous sensors, home networks, and a context-aware computing system. These homes are expected to offer many services such as intelligent air-conditioning, lighting control, health monitoring, and home security. In order to realize these services, many researchers have worked on various research topics including smart sensors with low power consumption, home network protocols, resident and location detection, context-awareness, and scenario and service control. This paper presents the real-time metabolic rate estimation method that is based on measured heart rate for human adaptive appliance (air-conditioner, lighting etc.). This estimation results can provide valuable information to control smart appliances so that they can adjust themselves according to the status of residents. The heart rate based method has been experimentally compared with the location-based method on a test bed.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.25-32
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• 2022

IR-UWB radar has been regarded as the most promising technology for non-contact respiration and heartbeat monitoring because of its ability of detecting slight motion even in submillimeter range. Measuring heart rate is most challenging since the chest movement by heartbeat is quite subtle and easily interfered with by a random body motion or background noise. Additionally, periodic sampling can be limited by the performance of computer that handles the radar signals. In this paper, we deploy Lomb-Scargle periodogram method that estimates heart rate even with irregularly sampled data and uneven signal amplitude. Lomb-Scargle periodogram is known as a method for finding periodicity in irregularly-sampled and noisy data set. We also implement a motion detection scheme in order to make the heart rate estimation pause when a random motion is detected. Our scheme is implemented using Novelda's X4M03 radar development kit and its corresponding drivers and Python packages. Experimental results show that the estimation with Lomb-Scargle periodogram yield more accurate heart rate than the method of measuring peak-to-peak distance.

• 대한임베디드공학회논문지
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• 제18권2호
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• pp.51-58
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• 2023

This paper proposes a deep learning method for estimating the heart rate from facial videos. Our proposed method estimates remote photoplethysmography (rPPG) signals to predict the heart rate. Although there have been proposed several methods for estimating rPPG signals, most previous methods can not be utilized in low-power single board computers due to their computational complexity. To address this problem, we construct a lightweight student model and employ a knowledge distillation technique to reduce the performance degradation of a deeper network model. The teacher model consists of 795k parameters, whereas the student model only contains 24k parameters, and therefore, the inference time was reduced with the factor of 10. By distilling the knowledge of the intermediate feature maps of the teacher model, we improved the accuracy of the student model for estimating the heart rate. Experiments were conducted on the UBFC-rPPG dataset to demonstrate the effectiveness of the proposed method. Moreover, we collected our own dataset to verify the accuracy and processing time of the proposed method on a real-world dataset. Experimental results on a NVIDIA Jetson Nano board demonstrate that our proposed method can infer the heart rate in real time with the mean absolute error of 2.5183 bpm.

• 대한의용생체공학회:의공학회지
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• 제28권4호
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• pp.441-448
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• 2007

Heart rate variability (HRV) in electrocardiogram (ECG) is an important index for understanding the health status of heart and the autonomic nervous system. Most HRV analysis approaches are based on the proper heart rate (HR) data. Estimation of heart rate is thus a key process in the HRV study. In this paper, we report an innovative method to estimate the heart rate. This method is mainly based on the concept of periodicity transform (PT) and instantaneous period (IP) estimate. The method presented is accordingly called the "PT-IP method." It does not require ECG R-wave detection and thus possesses robust noise-immune capability. While the noise contamination, ECG time-varying morphology, and subjects' physiological variations make the R-wave detection a difficult task, this method can help us effectively estimate HR for medical research and clinical diagnosis. The results of estimating HR from empirical ECG data verify the efficacy and reliability of the proposed method.

• 전기학회논문지
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• 제67권5호
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• pp.675-679
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• 2018

A novel heart rate estimation algorithm is presented based on normalized least-mean-square (NLMS) algorithm. This paper presented a three-step processing scheme for estimating heart rate from PPG signal with motion artifacts. The proposed active noise cancellation algorithm has low computational complexity compared to the NLMS algorithm. Experimental results show that the proposed algorithms perform similar with the previous algorithm under motion artifact noises.

• Journal of information and communication convergence engineering
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• 제8권1호
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• pp.112-115
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• 2010

The heart rate is the most important vital sign in diagnosing heart status. The simple method to measure the heart rate in the mobile healthcare device is using the PPG signal. In developing the mobile healthcare device using the PPG signal, the most important issue is the inaccuracy of the measured heart rate because the PPG signal is distorted from the user's motions. To improve the problem, this study proposed the new method that is to estimate the heart rate without an additional sensor in real life. The proposed method in this study is using the histogram filter. In order to evaluate the performance of the proposed method, the study compares its results with the moving average method in motion environment. According to the experimental results, the performance of the proposed method was more than 40% better than the performances of the MAF.