Browsing by Author "Huang, Po-Wei"

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A Contactless Sport Training Monitor Based on Facial Expression and Remote-PPG
(2017-01-01)
Wu, Bing-Fei
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Lin, Chun-Hsien
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Huang, Po-Wei
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Lin, Tzu-Min
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Chung, Meng-Liang
To successfully increase athletes' or exercisers' fitness and endurance, the factors of physiological signal, emotion, or the level of fatigue should be considered during the training program. Many clinical decision support systems can assist to monitor the exercisers by some wearable devices. And, the questionnaire should also be taken into account to produce a report. Such process is cumbersome, and the results are not objective. Furthermore, one may feel uncomfortable when wearing the devices during the training program. In this research, the Rating of Perceived Exertion (RPE) is expected to be estimated automatically without any wearable devices and questionnaires. A camera based heart rate detection algorithm and a fatigue expression feature extractor are fused to estimate the RPE value. The results show that our heart rate detection algorithm can be competitive to the wearable devices, and the trend of the detected heart rate is correlated to RPE. Moreover, the fatigue feature can help reduce the error of the estimation.
A Feature Selection Method for Vision-based Blood Pressure Measurement
(2018-01-01)
Fang, Yu-Fan
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Huang, Po-Wei
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Chung, Meng-Liang
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Wu, Bing-Fei
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電機工程學系
In this paper we investigate the latest vision-based method for systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurement. However, constantly blood pressure supervision needs sufficient medical equipment and may require the potential patients to tie a cuff, which is extremely inconvenient for them. What's more, continuously blood pressure measuring requires the patients to stay in the hospital and professional personnel to stand by. From the research before, we have learned that photoplethysmography (PPG) can be used to measure the blood pressure, which is known as cuffless blood pressure measurement. However, for the neonate and patients with empyrosis, photoplethysmography measuring device is still less practical and restricted in use due to the necessary contact for it to measure the systolic and diastolic blood pressure. Certain level of discomfort is still unavoidable with the use of PPG. We thus focus on remote PPG (rPPG); with green red difference (GRD) and Euler video magnification (EVM) and finite impulse response (FIR) bandpass filters, we are able to recover PPG signals from remote photoplethysmography. We propose a feature extraction measuring methods which yields a root mean square error for SBP as 11.22 mmHg and 7.83 mmHg for pulse pressure (PP) combined with the ANN model. For comparison, we've also used K nearest neighbor (KNN) and deep belief network-deep neural network (DBN-DNN).
A Motion Robust Remote-PPG Approach to Driver's Health State Monitoring
(2017-01-01)
Wu, Bing-Fei
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Chu, Yun-Wei
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Huang, Po-Wei
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Chung, Meng-Liang
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Lin, Tzu-Min
With the surging significance of personal health care, driver's physiological state is no longer negligible nowadays. Among all the indicators of health state in human, heart rate (HR) is one of the most cardinal indicators. The commonly used HR measurement is contact-type, might result in driver's distraction and discomfort in the vehicle applications. To cope with this problem, remote photoplethysmography (rPPG) is utilized to monitor HR at a distance via a web camera. Nevertheless, the rPPG is not without its flaw. The main concern of the rPPG technique is the potential not-robustness result from the arbitrary motion. Consequently, the contribution of this paper is to conquer the motion noise when the car is driving and the driver's health state is well monitored to enhance the public safety. The proposed algorithm is investigated in not only the indoor environment but as well the outdoor driving, which contains much more unpredictable motion. With k-nearest neighbor (kNN) classifier on chrominance-based features, the mean square error can be reduced from 30.6 to 2.79 bpm, approaching the medical instrument level. The proposed method can be applied to human improving driving safety for Advanced Driver Assistance Systems.
A Region of Interest Selection for Vision-Based Respiratory Rate Measurement in Sleeping Scenario
(2020-01-01)
Wu, Bing-Fei
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Lin, Chun-Han
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Huang, Po-Wei
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Chen, Kuan-Hung
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He, Da-Hong
Vision-based respiratory measurement can remotely measure respiratory information without affecting the sleep quality of the user. However, several scenarios during sleeping, such as when blankets cover areas of the face or body, can be a challenge when estimate respiratory rate (RR) due to the uncertainty of the region of interest (ROI). In this study, we first investigate the metrics and physical meanings of ROI selection from the perspectives of array signal processing and the concept of linear combining. Then, we propose an ROI detection algorithm based on both temporal and spatial consistency (TSC), which aims to extract the representative characteristics of respiration with fewer computational resources. Furthermore, a sleeping database, containing more than 50 hours of data, was built to investigate the performance of benchmarked ROI methods during long-term sleeping. The experimental results demonstrate that TSC attains the highest signal-to-noise ratio (SNR) at 20.6 dB and a relatively low elapsed time of 100.2 frames per second (fps). For the RR estimation accuracy, TSC reduces the mean absolute error from 1.65 breaths per minute (bpm) to 0.97 bpm.
Camera-based Heart Rate Measurement Using Continuous Wavelet Transform
(2017-01-01)
Wu, Bing-Fei
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Huang, Po-Wei
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Tsou, Tsong-Yang
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Lin, Tzu-Min
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Chung, Meng-Liang
Recent years have seen increased attention being given to measure Heart Rate with remote-PPG. Previous work mainly focused on optimal signal extraction from three channels of a webcam. Several robust signal extraction methods using either optic models or frequency analysis have been discussed before. After applied these algorithms, residual noise still emerges occasionally with short time duration and relative high energy. This paper investigates the cause of residual noise and then proposed an algorithm based on continuous wavelet transform (CWT) to reduce the highly dynamic residual noise. With noise reduction and signal reconstruction, the advantages of both CWT and FFT are integrated to cope with the motion/illuminance induced residual noise. The results obtained by this system show improved SNR ratio and Entropy under both motion and stationary conditions.
CF4-plasma-induced fluorine passivation effects on poly-Si TFTs with high-kappa Pr2O3 gate dielectric
(2008)
Chang, Chia-Wen
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Huang, Po-Wei
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Deng, Chih-Kang
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Huang, Jiun-Jia
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Chang, Hong-Ren
High-performance polycrystalline silicon thin-film transistors (poly-Si TFTs) integrating high-kappa Pr2O3 gate dielectric and fluorine-passivated poly-Si film are demonstrated. High gate capacitance density and thin equivalent-oxide thickness provided by the high-kappa Pr2O3 gate dielectric have the advantage of increasing the driving current capability of the TFT device, but an undesirable off-state leakage current could be introduced from the high electric field near the drain side. Introducing fluorine atoms into poly-Si films by employing a low-temperature CF4 plasma treatment can effectively passivate the trap states. With 10 W CF4 plasma treatment on poly-Si film, the electrical characteristics of poly-Si Pr2O3 TFTs can be significantly improved, including a steeper subthreshold swing, smaller threshold voltage, higher field-effect mobility, and better on/off current ratio compared with that without CF4 plasma treatment. The maximum off-state leakage current of the fluorine-passivated TFT is more than one order of magnitude lower than that of the control TFT. Furthermore, the incorporation of fluorine atoms by CF4 plasma treatment also improves the reliability of poly-Si Pr2O3 TFTs against hot carrier stressing, which is due to the formation of stronger Si-F bonds in place of weak Si-H bonds in the poly-Si channel and at the Pr2O3 gate dielectric/poly-Si interface. Therefore, high-performance and high-reliability poly-Si TFTs with Pr2O3 gate dielectric and CF4 plasma treatment on poly-Si film are suitable for active-matrix liquid crystal display application. (c) 2007 The Electrochemical Society.
Characteristic Comparison of Transversally Laminated Anisotropic Synchronous Reluctance Motor Fabrication Based on 2D Lamination and 3D Printing
(2015-01-01)
Zhang, Zhi-Yong
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Tsai, Mi-Ching
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Huang, Po-Wei
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Cheng, Chung-Wei
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Huang, Jian-Mi
Synchronous reluctance motors (SynRM) have been widely used because of their excellent performance, such as high efficiency and accurate speed control. This paper focused on the efficiency and starting ability of SynRM. Based on the view of steel sheet material and motor fabrication, the iron loss influences on SynRM regarding different grades of silicon steel are compared, and the performance of a squirrel cage assisted SynRM is investigated. Moreover, a new fabrication involving additive manufacturing (namely 3D printing) and a new high conductivity magnetic powder for facilitating the new SynRM fabrication process are proposed.
Characteristic Comparison of Transversally Laminated Anisotropic Synchronous Reluctance Motor Fabrication Based on 2D Lamination and 3D Printing
(2015)
Zhang, Zhi-Yong
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Tsai, Mi-Ching
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Huang, Po-Wei
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Cheng, Chung-Wei
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Huang, Jian-Mi
Synchronous reluctance motors (SynRM) have been widely used because of their excellent performance, such as high efficiency and accurate speed control. This paper focused on the efficiency and starting ability of SynRM. Based on the view of steel sheet material and motor fabrication, the iron loss influences on SynRM regarding different grades of silicon steel are compared, and the performance of a squirrel cage assisted SynRM is investigated. Moreover, a new fabrication involving additive manufacturing (namely 3D printing) and a new high conductivity magnetic powder for facilitating the new SynRM fabrication process are proposed.
Image Based Contactless Blood Pressure Assessment using Pulse Transit Time
(2017-01-01)
Huang, Po-Wei
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Lin, Chun-Hao
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Chung, Meng-Liang
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Lin, Tzu-Min
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Wu, Bing-Fei
Recent years have seen increased attention being given to Blood Pressure (BP) monitoring. Among all kinds of measurements, the monitors based on Pulse Transit Time (PTT) have gain plenty of attention due to its continuous and cuffless features. Additionally, several studies proposed a fancy way to estimate photoplethysmography (PPG) signal simply via a regular webcam. Nevertheless, literatures on issues of integrating these two advanced techniques have emerged on a slowly and scattered way. Furthermore, accuracy of BP prediction model based on PTT is often limited due to the lack of data. To address the above-mentioned problems, we proposed an image based BP measurement algorithm using k-nearest neighbor and transfer learning results from MIMICII database to real task. The study also introduces newly defined PTT features which are especially suitable for image based PPG and domain adaptation. Compared with the state-of-the-art algorithm, root mean square error of SBP evaluation has been reduced from 15.08 to 14.02.
Miniaturized Dual-Band Ring Coupler with Arbitrary Power Divisions Using Composite Right/Left-Handed Transmission Lines
(2014)
Chi, Pei-Ling
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Huang, Po-Wei
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資訊工程學系
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電機學院
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Department of Computer Science
In this paper, a ring coupler that allows for arbitrary power divisions in dual bands by use of the composite right/left-handed (CRLH) transmission structures is proposed. The presented 180 degrees coupler is composed of four 50-Omega CRLH branch lines and its power division ratio at either frequency is a function of electrical lengths of the constituent transmission lines. Thus, large power divisions can be simply carried out by properly designing electrical lengths of the integral lines instead of applying high-impedance transmission lines, which is impractical as the division ratios increase. For demonstration purpose, two examples were experimentally developed to realize the division ratios of 9/3 dB and 6/6 dB at 0.9/1.8 GHz. Compared to the compact 0.9-GHz coupler based on similar design concept in the literature, the fabricated couplers show further size reduction up to 21%.
Motion Resistant Image-Photoplethysmography Based on Spectral Peak Tracking Algorithm
(2018-01-01)
Wu, Bing-Fei
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Huang, Po-Wei
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Lin, Chun-Hsien
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Chung, Meng-Liang
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Tsou, Tsong-Yang
Medical fields have seen increasing attention being given to image based heart rate measurement in recent years. One of the major limitations is motion artifacts of subject's head. Although there have been many studies focusing on signal extraction using different parameters and models, the development of frequency domain analysis is emerging slowly and moving in many directions. In the field of contact photoplethysmography (PPG), recent studies employed the acceleration signals to assist their spectral peak tracking algorithms. Inspired by the development of contact PPG, we are proposing a motion resistant spectral peak tracking (MRSPT) framework which eliminates the motion artifacts by integrating facial motion signals. The effectiveness of MRSPT coupled with the optimal image-based PPG (iPPG) signal has been tested against the state-of-the-art spectral peak tracking algorithms, multi-channel spectral matrix decomposition (MC-SMD), and the maximum peak selection coupled with optimal iPPG signal (Optimal MPS). Compared with MC-SMD and Optimal MPS, MRSPT uplifts the success rate-10 (success rate-5), the probability in which the absolute error is below ten (five) beats per mins, from 54.7% (36.3%) with MC-SMD and 73.0% (61.3%) with Optimal MPS to 90.7% (75.7%) with MRSPT in motion scenarios where subject moves arbitrarily with different distance or lighting. MRSPT also enhances the success rate-10 (success rate-5) from 40.7% (26.3%) with MC-SMD and 57.4% (45.7%) with Optimal MPS to 73.4% (58.4%) with MRSPT in all seven motion conditions including driving and running. Averagely, the success rate-five of Optimal MRSPT surpass the success rate-10 of both Optimal MPS and MC-SMD.
Neural Network Based Luminance Variation Resistant Remote-Photoplethysmography for Driver's Heart Rate Monitoring
(2019-01-01)
Wu, Bing-Fei
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Chu, Yun-Wei
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Huang, Po-Wei
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Chung, Meng-Liang
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電機工程學系
The driver's physiological status has enormous value to public traffic safety and cannot be ignored nowadays. In addition, heart rate (HR) is one of the most important indicators of human's health status. When detecting the driver's HR, using traditional contact-type devices might bring about the driver's distraction or discomfort. On the contrary, the remote photoplethysmography (rPPG) technique is a better way to monitor a driver's HR in vehicle applications simply by using a web-camera without interfering the driver. Most of the rPPG studies intended to reduce the interference caused by facial motion or luminance changes in the indoor or controlled scenario, but there are relatively fewer discussions on outdoor scenarios. Consequently, the purpose of this paper is to enhance the rPPG technique to make it suitable for the outdoor driving scenarios and for monitoring the driver's HR in different weather conditions, including daytime and nighttime. We first utilize artificial neural network (ANN) and train multiple personalized ANN models for each driver. For predicting the drivers' HR beat more precisely, we propose the approach, adaptive neural network model selection (ANNMS), which adaptively selects a personalized ANN model based on different noise conditions. Our algorithm eliminates the effect of noises caused by the variations of facial luminance in eight outdoor driving scenarios. The proposed driver's HR beat monitoring system has been evaluated against the state-of-the-art rPPG techniques that are Chrominance signal-based (CHRO) and k-nearest neighbors-based (kNN) algorithms. Compared with the CHRO and kNN algorithms, the ANNMS reduces the mean absolute error from 14.71 bpm (CHRO) and 9.91 (kNN) to 4.51 bpm (ANNMS) and enhances the successrate-10, the probability in which the absolute error is below 10 bpm, from 44.1% (CHRO) and 56.3% (kNN) to 91.5% (ANNMS).
Remote Photoplethysmography Enhancement with Machine Leaning Methods
(2019-01-01)
Wu, Bing-Fei
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Huang, Po-Wei
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He, Da-Hong
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Lin, Chung-Han
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Chen, Kuan-Hung
Driver's physiological state is highly correlated to the traffic safety. An affordable and convenient way to monitor driver's physiological state is remote Photoplethysmography (rPPG). Earlier algorithms achieved high accuracy on measuring rPPG signals in stationary case. But in real cases, such as driving, rPPG signals might be corrupted with interference. To obtain higher Signal-to-Noise-Ratio (SNR) rPPG signals, three algorithms are proposed. The PCA spectral subtraction (PCA-SS) considers the spectrum of the environmental noise and utilizes the energy subtraction to reduce the noise. The machine learning methods, convolution autoencoder (CAE) and multi-channel convolution autoencoder (Multi-CAE), are adopted in order to enhance the rPPG signal. The test data we used are 187 videos recorded in stationary case, passenger case, and real driving situation. In driving situation, the Multi-CAE method, in comparison with the original method provided by W. Wang et al. [1] and G. De Haan et al. [2], achieves 33% & 35% reduction in MAE, RMSE respectively, and 11% improvement in success rate [3].
Vision-Based Instant Measurement System for Driver Fatigue Monitoring
(2020-01-01)
Tsai, Yin-Cheng
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Lai, Peng-Wen
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Huang, Po-Wei
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Lin, Tzu-Min
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Wu, Bing-Fei
In this paper, a vision-based physiological signal measurement system is proposed to instantly measure driver fatigue. A remote photoplethysmography (rPPG) signal is a type of physiological signal measured by a camera without any contact device, and it also retains the characteristics of the PPG, which is useful to evaluate fatigue. To solve the inconvenience caused by the traditional contact-based physiological fatigue detection system and to improve the accuracy, the system measures both the motional and physiological information by using one image sensor. In a practical application, the environmental noise would affect the measured signal, and therefore, we performed a preprocessing step on the signal to extract a clear signal. The experiment was designed in collaboration with Taipei Medical University, and a questionnaire-based method was used to define fatigue. The questionnaire that could directly react to the feeling of the subject was treated as our ground truth. The evaluated correlation was 0.89 and the root mean square error was 0.65 for ten-fold cross-validation on the dataset. The trend of driver fatigue could be evaluated without a contact device by the proposed system. This advantage ensures the safety of the driver and reliability of the system.