• 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.

• 한국멀티미디어학회논문지
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• 제24권11호
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• pp.1481-1491
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• 2021

Since most biosignals rely on contact-based measurement, there is still a problem in that it is hard to provide convenience to users by applying them to daily life. In this paper, we present a mobile application for estimating heart rate based on a deep learning model. The proposed application measures heart rate by capturing real-time face images in a non-contact manner. We trained a three-dimensional convolutional neural network to predict photoplethysmography (PPG) from face images. The face images used for training were taken in various movements and situations. To evaluate the performance of the proposed system, we used a pulse oximeter to measure a ground truth PPG. As a result, the deviation of the calculated root means square error between the heart rate from remote PPG measured by the proposed system and the heart rate from the ground truth was about 1.14, showing no significant difference. Our findings suggest that heart rate measurement by mobile applications is accurate enough to help manage health during daily life.

• 센서학회지
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• 제25권3호
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• pp.196-201
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• 2016

Rehabilitation exercises are the treatments designed to help patients who are in the process of recovery from injury or illness to restore their body functions back to the original status. However, many patients suffering from chronic diseases have found difficulties visiting hospitals for the rehabilitation program due to lack of transportation, cost of the program, their own busy schedules, etc. Also, the program usually contains a few medical check-ups which can cause patients to feel uncomfortable. In this paper, we develop a remote rehabilitation system with bio-signals by a stereo camera. A Kinect stereo camera manufactured by Microsoft corporation was used to recognize the body movement of a patient by using its infrared(IR) camera. Also, we detect the chest area of a user from the skeleton data and process to gain respiratory status. ROI coordinates are created on a user's face to detect photoplethysmography(PPG) signals to calculate heart rate values from its color sensor. Finally, rehabilitation exercises and bio-signal detecting features are combined into a Windows application for the cost effective and high performance remote rehabilitation system.

• Journal of Multimedia Information System
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• 제8권1호
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• pp.1-10
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• 2021

Heart Rate is a crucial physiological parameter that provides basic information about the state of the human body in the cardiovascular system, as well as in medical diagnostics and fitness assessments. At present day, it has been demonstrated that facial video-based photoplethysmographic signal captured using a low-cost RGB camera is possible to retrieve remote heart rate. Traditional heart rate measurement is mostly obtained by direct contact with the human body, therefore, it can result inconvenient for long-term measurement due to the discomfort that it causes to the subject. In this paper, we propose a non-contact-based remote heart rate measuring approach of the subject which depends on the color intensity variation of the subject's facial skin. The proposed method is applied in two regions of the subject's face, forehead and cheeks. For this, three different algorithms are used to measure the heart rate. i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The average accuracy for the three algorithms utilizing the proposed method was 89.25% in both regions. It is also noteworthy that the FastICA algorithm showed a higher average accuracy of more than 92% in both regions. The proposed method obtained 1.94% higher average accuracy than the traditional method based on average color value.

• 실천공학교육논문지
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• 제6권2호
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• pp.135-139
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• 2014

발표자료를 표시하는 프로그램 제어용 도구인 프레젠테이션 리모트 컨트롤러(presentation remote controller)를 발전시켜 단순한 페이지별 이동기능 외에 발표자의 긴장도를 실시간으로 확인하고 인체 반응 상태를 점검하며, 발표능력과 전달의도가 정확하지 못한 부분을 미리 확인하고 보완할 수 있는 지능 부가형 제어기 구성을 제안하고 성능을 시험한다. 제어기로는 스마트 폰이 사용되며 블루투스(bluetooth) 모듈을 인터페이스로 하여, 실행되는 프로세서에 접속하고 맥박 신호를 발표자 신체신호로 검출하여 실시간(150 ms 이내)으로 인체 상태를 인식하고 기록한다. 저장된 인체신호 데이터는 가공/처리되어 발표 능력과 자신감 향상의 자료로 이용한다. 이러한 일련의 과정은 4년제 대학의 졸업예정자를 대상으로 20주의 비정규적인 캡스톤디자인 활동을 통해 이루어진 결과이다.