Collaborative Processing of Wearable and Ambient Sensor System for Blood Pressure Monitoring

This paper describes wireless wearable and ambient sensors that cooperate to monitor a person’s vital signs such as heart rate and blood pressure during daily activities. Each wearable sensor is attached on different parts of the body. The wearable sensors require a high sampling rate and time synchronization to provide a precise analysis of the received signals. The trigger signal for synchronization is provided by the ambient sensors, which detect the user’s presence. The Bluetooth and IEEE 802.15.4 wireless technologies are used for real-time sensing and time synchronization. Thus, this wearable health-monitoring sensor response is closely related to the context in which it is being used. Experimental results indicate that the system simultaneously provides information about the user’s location and vital signs, and the synchronized wearable sensors successfully measures vital signs with a 1 ms resolution

Estimating pulse wave velocity using mobile phone sensors

Pulse wave velocity has been recognised as an important physiological phenomenon in the human body, and its measurement can aid in the diagnosis and treatment of chronic diseases. It is the gold standard for arterial stiffness measurements, and it also shares a positive relationship with blood pressure and heart rate. There exist several methods and devices via which it can be measured. However, commercially available devices are more geared towards working health professionals and hospital settings, requiring a significant monetary investment and specialised training to operate correctly. Furthermore, most of these devices are not portable and thus generally not feasible for private home use by the common individual. Given its usefulness as an indicator of certain physiological functions, it is expected that having a more portable, affordable, and simple to use solution would present many benefits to both end users and healthcare professionals alike. This study investigated and developed a working model for a new approach to pulse wave velocity measurement, based on existing methods, but making use of novel equipment. The proposed approach made use of a mobile phone video camera and audio input in conjunction with a Doppler ultrasound probe. The underlying principle is that of a two-point measurement system utilising photoplethysmography and electrocardiogram signals, an existing method commonly found in many studies. Data was collected using the mobile phone sensors and processed and analysed on a computer. A custom program was developed in MATLAB that computed pulse wave velocity given the audio and video signals and a measurement of the distance between the two data acquisition sites. Results were compared to the findings of previous studies in the field, and showed similar trends. As the power of mobile smartphones grows, there exists potential for the work and methods presented here to be fully developed into a standalone mobile application, which would bring forth real benefits of portability and cost-effectiveness to the prospective user base

Experimental Demonstration of Accurate Noncontact Measurement of Arterial Pulse Wave Displacements Using 79-GHz Array Radar

In this study, we present a quantitative evaluation of the accuracy of simultaneous array-radar-based measurements of the displacements caused at two parts of the human body by arterial pulse wave propagation. To establish the feasibility of accurate radar-based noncontact measurement of this pulse wave propagation, we perform experiments with four participants using a 79-GHz millimeter-wave ultra-wideband multiple-input multiple-output array radar system and a pair of laser displacement sensors. We evaluate the accuracy of the pulse wave propagation measurements by comparing the displacement waveforms that are measured using the radar system with the corresponding waveforms that are measured using the laser sensors. In addition, to evaluate the estimates of the pulse wave propagation channels, we compare the impulse response functions that are calculated from the displacement waveforms obtained from both the radar data and the laser data. The displacement waveforms and the impulse responses both demonstrated the good agreement between the results of the radar and laser measurements. The normalized correlation coefficient between the impulse responses obtained from the radar and laser data on average was as high as 0.97 for the four participants. The results presented here strongly support the feasibility of accurate radar-based noncontact measurement of arterial pulse wave propagation

Modeling of Arterial Stiffness using Variations of Pulse Transit Time

In this paper, a finite element (FE) modeling is used to model effects of the arterial stiffness on the different signal patterns of the pulse transit time (PTT). Several different breathing patterns of the three subjects are measured with PTT signal and corresponding finite element model of the straight elastic artery is applied. The computational fluid-structure model provides arterial elastic behavior and fitting procedure was applied in order to estimate Young's module of stiffness of the artery. It was found that approximately same elastic Young's module can be fitted for specific subject with different breathing patterns which validate this methodology for possible noninvasive determination of the arterial stiffness

A health-shirt using e-textile materials for the continuous monitoring of arterial blood pressure.

Chan, Chun Hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 77-84).Abstracts in Chinese and English.Acknowledgment: --- p.i摘要 --- p.iiAbstract --- p.ivList of Figure --- p.viList of Table --- p.viiiContent Page --- p.ixChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- The Difficulties --- p.1Chapter 1.2 --- The Solution --- p.2Chapter 1.3 --- Goal of the Present Work --- p.2Chapter Chapter 2 --- Background and Methodology --- p.3Chapter 2.1 --- Hypertension Situation and Problems Around the World --- p.3Chapter 2.1.1 --- Blood Pressure Variability (BPV) --- p.4Chapter 2.2 --- Blood Pressure Measuring Methods --- p.5Chapter 2.2.1 --- Traditional Blood Pressure Meters --- p.6Chapter 2.2.2 --- Limitation of Commercial Blood Pressure Meters --- p.7Chapter 2.2.3 --- Pulse-Transit-Time (PTT) Based Blood Pressure Measuring Watch --- p.7Chapter 2.3 --- Wearable Body Sensors Network / System --- p.8Chapter 2.4 --- Current Status of e-Textile Garment --- p.9Chapter 2.4.1 --- Blood Pressure Measurement in e-Textile Garment --- p.13Chapter 2.5 --- Wearable Intelligent Sensors and System for e-Health (WISSH) --- p.15Chapter 2.5.1 --- "Monitoring, Connection and Display" --- p.15Chapter 2.5.2 --- Treatment --- p.16Chapter 2.5.3 --- Alarming --- p.17Chapter Chapter 3 --- "A h-Shirt to Non-invasive, Continuous Monitoring of Arterial Blood Pressure" --- p.18Chapter 3.1 --- Design and Inner Structure of h-Shirt --- p.18Chapter 3.1.1 --- Choose of e-Textile Material --- p.21Chapter 3.1.2 --- Design of ECG Circuit --- p.23Chapter 3.1.3 --- Design of PPG Circuit --- p.26Chapter 3.2 --- Blood Pressure Estimation Using Pulse-Transit-Time Algorithm --- p.28Chapter 3.2.1 --- Principal --- p.28Chapter 3.2.2 --- Equations --- p.29Chapter 3.2.3 --- Calibration --- p.29Chapter 3.3 --- Performance Tests on h-Shirt --- p.30Chapter 3.3.1 --- Test I: BP Measurement Accuracy --- p.30Chapter 3.3.2 --- Test I: Procedure and Protocol --- p.30Chapter 3.3.3 --- Test I-Results --- p.31Chapter 3.3.4 --- Test II: Continuality BP Estimation Performance --- p.31Chapter 3.3.5 --- Test II - Experiment Procedure and Protocol --- p.32Chapter 3.3.6 --- Test II - Experiment Result --- p.33Chapter 3.3.7 --- Test II 一 Discussion --- p.43Chapter 3.4 --- Follow-up Tests on ECG Circuit --- p.47Chapter 3.4.1 --- Problems --- p.47Chapter 3.4.2 --- Assumptions --- p.48Chapter 3.4.3 --- Experiment Protocol and Setup --- p.48Chapter 3.4.4 --- Experiment Results --- p.53Chapter 3.4.5 --- Discussion --- p.56Chapter Chapter 4: --- Hybrid Body Sensor Network in h-Shirt --- p.59Chapter 4.1 --- A Hybrid Body Sensor Network --- p.59Chapter 4.2 --- Biological Channel Used in h-Shirt --- p.60Chapter 4.3 --- Tests of Bio-channel Performance --- p.62Chapter 4.3.1 --- Experiment Protocol --- p.62Chapter 4.3.2 --- Results --- p.62Chapter 4.4 --- Discussion and Conclusion --- p.63Chapter Chapter 5: --- Conclusion and Suggestions for Future Works --- p.66Chapter 5.1 --- Conclusion --- p.66Chapter 5.1.1 --- Structure of h-Shirt --- p.66Chapter 5.1.2 --- Blood Pressure Estimating Ability of h-Shirt --- p.67Chapter 5.1.3 --- Tests and Amendments on h-Shirt ECG Circuit --- p.67Chapter 5.1.4 --- Hybrid Body Sensor Network in h-Shirt --- p.67Chapter 5.2 --- Suggestions for Future Work --- p.68Chapter 5.2.1 --- Further Development of Bio-channel Biological Model --- p.68Chapter 5.2.2 --- Positioning and Motion Sensing with h-Shirt --- p.69Chapter 5.2.3 --- Implementation of Updated Advance Technology into h-Shirt --- p.69Appendix: Non-invasive BP Measuring Device - Finometer --- p.71Reference: --- p.7

A pervasive system for real-time blood pressure monitoring

Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology