- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Improved Sparse Adaptive Algorithms for Accurate Non-contact Heartbeat Detection Using Time-Window-Variation Technique
摘要： Recently, a sparse adaptive algorithm termed zero-attracting sign least-mean-square (ZA-SLMS), has been clarified to be able to reconstruct robustly heartbeat spectrum by Doppler radar signal. However, since the strengths of noise evidently differ under different body motions, the sparse heartbeat spectra cannot be always acquired accurately by the constant regularization parameter (REPA) that balances the gradient correction and the sparse penalty, applying in the ZA-SLMS algorithm. In this paper, an improved ZA-SLMS algorithm is proposed by introducing adaptive REPA (AREPA), where the proportion of sparse penalty is adjusted based on the standard deviation of radar data. Moreover, to enhance the stability of heartbeat detection, a time-window-variation (TWV) technique is further introduced in the improved ZA-SLMS algorithm, considering the fact that the position of spectral peak associated with the heart rate (HR) is stable when the length of time window changes within a short period. Experimental results measured against five subjects validated that our proposal reliably improves the error of HR estimation than the standard ZA-SLMS algorithm.
关键词： heartbeat detection,time-window-variation,Doppler radar,regularization parameter,sparse adaptive algorithm
更新于2025-09-23 15:23:52
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[Institution of Engineering and Technology 12th European Conference on Antennas and Propagation (EuCAP 2018) - London, UK (9-13 April 2018)] 12th European Conference on Antennas and Propagation (EuCAP 2018) - Remote Vital Sign Recognition through Machine Learning augmented UWB
摘要： This paper describes an experimental demonstration of machine learning (ML) techniques supplementing radar to distinguish and detect vital signs of users in a domestic environment. This work augments an intelligent location awareness system previously proposed by the authors. That research employed Ultra-Wide Band (UWB) radar complemented by supervised machine learning techniques to remotely identify a person’s room location via ?oor plan training and time stamp correlations. Here, the remote breathing and heartbeat signals are analyzed through Short Term Fourier Transformation (STFT) to determine the Micro-Doppler signature of those vital signs in different room locations. Then, Multi-Class Support Vector Machine (MC-SVM) is implemented to train the system to intelligently distinguish between vital signs during different activities. Statistical analysis of the experimental results supports the proposed algorithm. This work could be used to further understand, for example, how active older people are by engaging in typical domestic activities.
关键词： Short Term Fourier Transform (STFT),Breathing,Ultra-Wide Band (UWB),Multi-Class Support Vector Machine (MC-SVM),Heartbeat,Indoor Positioning System (IPS)
更新于2025-09-23 15:22:29
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Higha??Accuracy Photoplethysmography Array Using Neara??Infrared Organic Photodiodes with Ultralow Dark Current
摘要： Reflectance oximeters based on organic photodiode (OPD) arrays offer the potential to map blood pulsation and oxygenation via photoplethysmography (PPG) over a large area and beyond the traditional sensing locations. Here, an organic reflectance PPG array based on 16 × 16 OPD pixels is developed. The individual pixels exhibit near-infrared sensitivity up to ≈950 nm and low dark current density in the order of 10?6 mA cm?2. This results in high-quality PPG signals. Analysis of the full PPG waveform yields insight on the artery stiffness and the quality of blood circulation, demonstrating the potential of these arrays beyond pulse oximetry and heart-rate calculation.
关键词： photoplethysmography,organic photodiodes,bulk heterojunction,heartbeat,pulse oximetry
更新于2025-09-23 15:19:57
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[IEEE 2018 5th International Conference on Information Science and Control Engineering (ICISCE) - Zhengzhou, China (2018.7.20-2018.7.22)] 2018 5th International Conference on Information Science and Control Engineering (ICISCE) - A Portable 24GHz Doppler Radar System for Distant Human Vital Sign Monitoring
摘要： Vital sign detection using Doppler radar can be potentially applied in health care and home monitoring. While most of developed Doppler radars are designed in laboratory environments, this paper introduces a portable 24 GHz Doppler radar using commercial modules at low cost. The Doppler radar system adopts the commercially available 24GHz transceiver module to obtain analog quadrature signals, the signals are digitalized using analog-to-digital conversion in microcontroller, then sent to laptop or mobile phone via a series to WIFI module. All the modules are implemented on a printed circuit board to achieve the small size. Experiments validate that the Doppler radar can sense human respiration and heartbeat rates successfully using time frequency based signal processing algorithms.
关键词： vital sign detection,Doppler radar,time frequency analysis,heartbeat and respiration
更新于2025-09-19 17:15:36
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IoT-Based Health Monitoring System Using BeagleBone Black with Optical Sensor
摘要： There is an increase in the number of chronic and heart diseases due to work culture etc. The current hospital-centric system is becoming inefficient to treat patients that demand immediate attention and this can efficiently be implemented by using the Internet of Things (IoT) technology. The aim of this paper is to implement IoT-based health monitoring system which measures temperature, blood pressure, and heartbeat of a patient located remotely and send the data to the doctor for analyzing the condition of the patient. And also an optical light sensor is used to check the light condition in the patient room and based on the sensor value the light will be controlled (ON/OFF). The system is implemented using a BeagleBone Black (BBB) development board. This model saves the work time of the doctors to check the patient’s condition. By using the Global System for Mobile communication (GSM), the patient’s data is sent to the cloud through which the doctor can monitor the parameters anywhere in the world using the mobile application or web page.
关键词： GSM module,BeagleBone Black,temperature sensor,optical light sensor,heartbeat sensor,blood pressure sensor
更新于2025-09-12 10:27:22
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A Stochastic Gradient Approach for Robust Heartbeat Detection with Doppler Radar Using Time-Window-Variation Technique
摘要： Heart rate variability (HRV) indicates health condition and mental stress. The development of non-contact heart rate (HR) monitoring technique with Doppler radar is attracting great attentions. However, the performance of heartbeat detection via radar signal easily degrades, due to respiration and body motion. In this paper, first, a stochastic gradient approach is applied to reconstruct high-resolution spectrum of heartbeat, by proposing the zero-attracting sign least-mean-square (ZA-SLMS) algorithm. To correct the quantized gradient of cost function, and penalize the sparse constraint on the updating spectrum, more accurate heartbeat spectrum is reconstructed. Then, to better adapt to the noises with different strengths caused by subjects’ movements, an adaptive regularization parameter (AREPA) is introduced in the ZA-SLMS algorithm as an improved variant, which can adaptively regulate the proportion between gradient correction and sparse penalty. Moreover, in view of the stability of location of spectral peak associated with HR when the size of time window slightly changes, a time-window-variation (TWV) technique is further incorporated in the improved ZA-SLMS (IZA-SLMS) algorithm, for more stable HR estimation. Through the experiments on five subjects, our proposal is demonstrated to bring a significant improvement of accuracy against existing detection methods. Specifically, the IZA-SLMS algorithm with TWV achieves the smallest average error of 3.79 beats per minute (BPM), when subjects type with a laptop.
关键词： Doppler radar,Non-contact heartbeat detection,sparse spectrum reconstruction (SSR),adaptive filter,time-window-variation (TWV),heart rate (HR)
更新于2025-09-09 09:28:46
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - A 5-ms Error, 22-μA Photoplethysmography Sensor using Current Integration Circuit and Correlated Double Sampling
摘要： This paper presents a low-power Photoplethysmography (PPG) sensing method. The PPG is commonly used in recent wearable devices to detect cardiovascular information including heartbeat. The heartbeat is useful for physical activity and stress monitoring. However, the PPG circuit consumes large power because it consists of LED and photodiode. To reduce its power consumption without accuracy degradation, a cooperative design of circuits and algorithms is proposed in this work. A straightforward way to reduce the power is intermittent driving of LED, but there is a disadvantage that the signal is contaminated by a noise while circuit switching. To overcome this problem, we introduce correlated double sampling (CDS) method, which samples an integration circuit output twice with short intervals after the LED turns on and uses the difference of these voltage. Furthermore, an up-conversion method using linear interpolation, and an error correction using autocorrelation are introduced. The proposed PPG sensor, which consists of the LED, the photodiode, the current integration circuit, a CMOS switch, an A/D converter, and an MCU, is prototyped. It is evaluated by actual measurement with 22-year-old subject. The measurement results show that 22-μA total current consumption is achieved with 5-ms mean absolute error.
关键词： wearable devices,low-power,heartbeat monitoring,Photoplethysmography (PPG),autocorrelation,correlated double sampling (CDS)
更新于2025-09-04 15:30:14