Respiration Monitoring Systems Research Articles

Ultrasonic-based submillimeter ranging system for contactless respiration monitoring

Wireless sensing technologies have exploited various methods for contactless respiration monitoring. One of the main methods is to use an acoustic beam to cover the chest and abdomen with real-time and high-resolution ranging. The system with high ranging resolution can detect small distance transformations, further affecting the precision of respiratory monitoring. However, the traditional range-based respiration monitoring systems are limited by an insufficient sampling rate and poor synchronization. The ranging resolution is between millimeters and centimeters. In this study, we introduced a novel, high-precision ultrasound-based system for contactless respiration monitoring. First, we developed a convenient and high-performance ultrasonic transceiver platform with universal serial bus audio architecture to eliminate the above limitations. The developed platform provides a foundation for acoustic sensing. Furthermore, based on this platform, we designed a cross correlation sine-frequency modulation continuous wave ranging method and a real-time respiration wave recovery method. The median error in monitoring respiration for 12 participants in three different sleep postures is 0 breaths/min, and the maximum error is 0.58 breaths/min. The proposed system has strong robustness concerning different respiration rates, long-term continuous respiration monitoring, and simulated apnea.

Wi-Breath: A WiFi-Based Contactless and Real-Time Respiration Monitoring Scheme for Remote Healthcare.

Respiration rate is an important healthcare indicator, and it has become a popular research topic in remote healthcare applications with Internet of Things. Existing respiration monitoring systems have limitations in terms of convenience, comfort, and privacy, etc. This paper presents a contactless and real-time respiration monitoring system, the so-called Wi-Breath, based on off-the-shelf WiFi devices. The system monitors respiration with both the amplitude and phase difference of the WiFi channel state information (CSI), which is sensitive to human body micro movement. The phase information of the CSI signal is considered and both the amplitude and phase difference are used. For better respiration detection accuracy, a signal selection method is proposed to select an appropriate signal from the amplitude and phase difference based on a support vector machine (SVM) algorithm. Experimental results demonstrate that the Wi-Breath achieves an accuracy of 91.2% for respiration detection, and has a 17.0% reduction in average error in comparison with state-of-the-art counterparts.

Offset Compensation in Resistive Stretch Sensors Using Low-Frequency Feedback Topology

Respiration monitoring systems play an important role in healthcare and fitness. For this purpose, resistive stretch sensors are frequently used, which are cheap and simple in operation. However, they are not free from drawbacks. Varying offset due to patient movement, low signal amplitude, as well as susceptibility to interference, can all pose serious challenges. In this paper, a novel signal conditioning circuit for a resistive respiration sensor is proposed that alleviates some of the above problems. Namely, the proposed low-frequency feedback topology improves the dynamic range by offset compensation, sustaining a high signal amplification. Further advantages of the new configuration are the phase shift of 0.5 degrees in the band of interest and higher gain for the respiration signal than for the offset. The topology was proved to correctly represent signal amplitude changes, as well as to be able to sample human respiration in the home environment. However, the circuit shows some nonlinear behavior around resistance discontinuity points–settling time after body position change of the patient, which can be as long as 40 s. The circuit was tested both in bench tests and in the prototype of a respiratory polygraphy device during actual sleep apnea examinations. The results indicate that resistive stretch sensors, along with low-frequency feedback topology, are a promising development path for future respiration monitoring devices.

Emerging Development of Auto-Charging Sensors for Respiration Monitoring.

In recent years, the development of biomedical monitoring systems, including respiration monitoring systems, has been accelerated. Wearable and implantable medical devices are becoming increasingly important in the diagnosis and management of disease and illness. Respiration can be monitored using a variety of biosensors and systems. Auto-charged sensors have a number of advantages, including low cost, ease of preparation, design flexibility, and a wide range of applications. It is possible to use the auto-charged sensors to directly convert mechanical energy from the airflow into electricity. The ability to monitor and diagnose one's own health is a major goal of auto-charged sensors and systems. Respiratory disease model output signals have not been thoroughly investigated and clearly understood. As a result, figuring out their exact interrelationship is a difficult and important research question. This review summarized recent developments in auto-charged respiratory sensors and systems in terms of their device principle, output property, detecting index, and so on. Researchers with an interest in auto-charged sensors can use the information presented here to better understand the difficulties and opportunities that lie ahead.

Portable Respiration Monitoring System with an Embroidered Capacitive Facemask Sensor.

Respiration monitoring is a very important indicator of health status. It can be used as a marker in the recognition of a variety of diseases, such as sleep apnea, asthma or cardiac arrest. The purpose of the present study is to overcome limitations of the current state of the art in the field of respiration monitoring systems. Our goal was the development of a lightweight handheld device with portable operation and low power consumption. The proposed approach includes a textile capacitive sensor with interdigitated electrodes embroidered into the facemask, integrated with readout electronics. Readout electronics is based on the direct interface of the capacitive sensor and a microcontroller through just one analog and one digital pin. The microcontroller board and sensor are powered by a smartphone or PC through a USB cable. The developed mobile application for the Android™ operating system offers reliable data acquisition and acts as a bridge for data transfer to the remote server. The embroidered sensor was initially tested in a humidity-controlled chamber connected to a commercial impedance analyzer. Finally, in situ testing with 10 volunteering subjects confirmed stable operation with reliable respiration monitoring.

MXene/MWCNT electronic fabric with enhanced mechanical robustness on humidity sensing for real-time respiration monitoring

Humidity sensing-based wearable respiration monitoring systems have promising application prospects in providing the critical health information. Currently, due to the inherent flexibility and compatibility, fabric-based wearables have attracted tremendous interest in humidity sensing. Herein, we proposed a humidity sensor made from fabric coated with MXene and multi-walled carbon nanotubes (MWCNT). The sensor showed 265% response at 90% relative humidity (RH) and great response stability under deformation. The humidity response of MXene/MWCNT fabric (MC-fabric) sensor only varied 7% under stretch, which is greatly alleviated compared with pristine MXene fabric sensor (35% under stretch). The MC-fabric sensor, integrated into a mask, can accurately identify diverse human respiration patterns despite the deformation caused by motions. This work not only offers a facile modification strategy to enhance the sensing robustness under deformation, but also provides a feasible method for real-time breath analysis.

IoT enabled distributed cardiac monitoring using Fiber Bragg Grating (FBG) sensing technology

Monitoring of physiological parameters during treatment procedure is vital, thus recommended in various medical conditions such as claustrophobia, pediatric, implanted artificial pacemakers, dementia, coma, and local anesthesia conditions etc. Further, electronic equipment and sensors are not allowed to be used everywhere inside the healthcare facility and medical experts are typically not aware about the patient’s cardiac conditions. Therefore, there is a requirement of such additional systems in the already existing or new healthcare facilities. As of now, many research works have been published for design and test various sensing schemes using FBGs. However, there is no cost effective solution available yet, which is in practice at commercial healthcare facilities. The proposed work provides an experimental validation of such cardiac and respiration monitoring systems to develop a test case in a real scenario. The design considerations and process of the sensor element is discussed in details in this article. In addition to that the implementation of Internet of Things (IoT) based approach for remote monitoring and notification mechanism are portrayed in this article.

A Pervasive Respiratory Monitoring Sensor for COVID-19 Pandemic.

Goal: The SARS-CoV-2 viral infection could cause severe acute respiratory syndrome, disturbing the regular breathing and leading to continuous coughing. Automatic respiration monitoring systems could provide the necessary metrics and warnings for timely intervention, especially for those with mild symptoms. Current respiration detection systems are expensive and too obtrusive for any large-scale deployment. Thus, a low-cost pervasive ambient sensor is proposed. Methods: We will posit a barometer on the working desk and develop a novel signal processing algorithm with a sparsity-based filter to remove the similar-frequency noise. Three modes (coughing, breathing and others) will be conducted to detect coughing and estimate different respiration rates. Results: The proposed system achieved 97.33% accuracy of cough detection and 98.98% specificity of respiration rate estimation. Conclusions: This system could be used as an effective screening tool for detecting subjects suffering from COVID-19 symptoms and enable large scale monitoring of patients diagnosed with or recovering.

Reference Free Incremental Deep Learning Model Applied for Camera-Based Respiration Monitoring

The article describes a reference and training set free incrementally trained deep learning algorithm for camera-based respiration monitoring systems. The algorithm uses a model based discriminator to find salient areas having respiration like periodic motion. It stores the first principle component of the found waveforms into two slowly growing set along with negative, uncorrelated motion patterns. Using these samples, it trains a deep neural network classifier incrementally to recognize respiration from sudden and motion intensive situations. The classifier had no forgetting mechanism and it is able to adapt quickly the changing respiration patterns and conditions. The algorithm has been validated in a total of 24 hours diverse recording captured in the neonatal intensive care unit (NICU) of the I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> Dept. of Pediatrics and, II. Dept. of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary and in the COHFACE publicly available dataset of adult subjects. The clinical data set evaluation resulted in mean absolute error (MAE) 6.9 and root mean squared error (RMSE) of 9.8 breaths per minute, respectively, the MAE was below 5 breaths per minute for over 50% of the time. The algorithm was assessed in the COHFACE dataset of adult subjects as well with respiration estimation MAE and RMSE values of 0.95 and 1.7 breaths per minute.

Contactless Respiration Monitoring Using Ultrasound Signal With Off-the-Shelf Audio Devices

Recent years have witnessed advances of Internet of Things technologies and their applications to enable contactless sensing and elderly care in smart homes. Continuous and real-time respiration monitoring is one of the important applications to promote assistive living for elders during sleep and attracted wide attention in both academia and industry. Most of the existing respiration monitoring systems require expensive and specialized devices to sense chest displacement. However, chest displacement is not a direct indicator of breathing and thus false detection may often occur. In this paper, we design and implement a real-time and contactless respiration monitoring system by directly sensing the exhaled airflow from breathing using ultrasound signals with off-the-shelf speaker and microphone. Exhaled airflow from breathing can be regarded as air turbulence, which scatters the sound wave and results in Doppler effect. Our system works as an acoustic radar which transmits sound wave and detects the Doppler effect caused by breathing airflow. We mathematically model the relationship between the Doppler frequency change and the direction of breathing airflow. Based on this model, we design a minimum description length-based algorithm to effectively capture the Doppler effect caused by exhaled airflow. We conduct extensive experiments with 25 participants (7 elders, 2 young kids, and 16 adults, including 11 females and 14 males) in four different rooms. The participants take four different sleep postures (lying on one’s back, on right/left side, and on one’s stomach) in different positions of the bed. Experiment results show that our system achieves a median error lower than 0.3 breaths/min (2%) for respiration monitoring and can accurately identify Apnea. The results also demonstrate that the system is robust to different respiration styles (shallow, normal, and deep), respiration rate variation, ambient noise, sensing distance variation (within 0.7 m), and transmitted signal frequency variation.

Contactless Respiration Monitoring Via Off-the-Shelf WiFi Devices

Non-invasive human sensing based on radio signals has attracted numerous research interests in recent years. Previous work mainly focused on detecting the presence of a person or identifying human gestures and activities. In this paper, we show that with off-the-shelf WiFi devices, fine-grained respiration information of a person under different sleeping positions can be extracted successfully. We do this by introducing a breath monitoring system based on WiFi signals. This system adopts off-the-shelf WiFi devices to continuously collect the fine-grained wireless channel state information (CSI) around a person. From the CSI, the rhythmic patterns associated with respiration and abrupt changes due to the body movement are identified. Compared to existing respiration monitoring systems that usually require special devices attached to human body, this system is completely contactless. In addition, different from many vision-based sleep monitoring systems, it is robust to low-light environments and does not raise privacy concerns. Preliminary testing results show that our system can reliably track a person's respiration reliably in different sleeping postures.

Noncontact respiration rate monitoring based on sensing exhaled air

Respiration rate is the average number of times air is inhaled and exhaled per minute. Respiration rate is an important indicator of a person’s health and therefore, it needs to be measured accurately. Existing respiration monitoring systems are generally contact based that means the sensing element needs to be attached to the subject's body. The attached sensor can cause distress in some children, affecting their respiration rate. The device can also become dislodged interrupting the monitoring. This work presents an air flow sensing approach to noncontact respiration rate monitoring. The exhaled air is guided through a small funnel to a chamber that contains a heating element. The heated air leaves the chamber and is then detected by a thermistor that converts the air flow temperature variations to an electrical signal. The signal is amplified, filtered and digitised. Signal processing techniques are used to extract respiration rate from the signal in real time. The device provides respiration rate at distances from 15 to 30 cm from the subject’s face.

SU-D-144-02: Four-Dimensional Cone Beam CT for a Small Animal Image Guided Radiation Therapy System Without Use of An External Respiratory Monitoring System

Purpose: To develop four‐dimensional cone beam CT for an image guided radiation system for small animals without the use of external respiration monitoring systems. Methods: Nine mice were scanned using the cone beam CT capabilities of an X‐Rad 225Cx system. The mice were anesthetized with 1.5 to 3% isoflurane and scanned (60 kVp and 4 mA) at a sampling rate of 15 fps and gantry rotation rate of 0.5 rpm to yield a total of approximately 1800 975×975 projection images. The projections were used to obtain a respiratory signal using a modified Amsterdam Shroud method. Inspirations appeared approximately as vertical lines in the Amsterdam Shroud image due to the small ratio of inspiration to expiration times in mice anesthetized with isoflurane. The locations of these lines were extracted using vertical edge detection, dilation and projection onto one dimension. Four‐dimensional cone beam CT images (voxel size 0.1 × 0.1 × 0.3 mm) were then obtained using these peak inspirations to sort the projections into 6 phases. The peak inspirations of two mice were manually extracted from the projections to test the peak extraction method. Results: 4D CBCT images were successfully created for each mouse with minimal streak artifacts. Some blurring in the initial phase was seen due to the sharpness of the inspiration peak. The peak finding algorithm determined the peaks correctly to within an average of 0.7 and 0.4 projections for the first and second mouse, respectively. 82% and 89% of the projections were sorted into the correct phase bin for the two mice. Conclusion: The four‐dimensional CBCT method developed here can be used to analyze lung motion in mice and assist in preclinical targeted radiation therapy studies.

土壤呼吸组分分离技术研究进展

PDF HTML阅读 XML下载 导出引用 引用提醒 土壤呼吸组分分离技术研究进展 DOI: 10.5846/stxb201207191027 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究发展计划（973项目）资助项目（2012CB955904）；国家科技支撑计划资助项目（2013BAD11B03）；国家自然科学基金资助项目（71103186）；中央级公益性科研院所基本科研业务费专项资助项目（BSRF201003） Progress on techniques for partitioning soil respiration components and their application in cropland ecosystem Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:分离土壤呼吸组分是理解陆地生态系统碳循环的重要步骤，研究农田生态系统土壤呼吸组分的呼吸过程和机理对促进农业温室气体减排和碳汇增加、气候变化适应、保障粮食安全以及推动农业可持续发展都具有积极意义。综述了近年来土壤呼吸组分分离的理论依据、主要技术及分类，系统比较了现有技术的优势、劣势和应用领域，并总结了土壤呼吸组分分离技术在国内外农田生态系统中的应用情况。由于多数分离技术在森林生态系统的相关研究中发展而来，它们在农田生态系统的应用十分有限，目前应用以同位素法、根分离法和回归法为主。由于土壤呼吸理论划分和分离方法的差异，不同研究结果之间往往难以比较。分离技术的发展有赖于土壤呼吸源分离理论的进一步发展，未来土壤呼吸组分分离研究的主要方向在于：（1）利用现有观测技术促进组分集成分析法和根分离法在农田生态系统中的应用，强化土壤呼吸组分和环境因子的同步观测，准确评估农田碳收支；（2）利用定位观测数据开展大尺度模型研究，改进和重构现有全球碳模型的碳氮过程，并在其中考虑重要的土壤呼吸过程；（3）利用FACE试验评估气候变化对土壤呼吸组分的影响和土壤-植物碳循环的适应机制；（4）分析呼吸组分与植物-土壤-养分的交互作用，评估农田管理措施的综合影响。 Abstract:Partitioning soil respiration components is a very important step before we understand the carbon cycling in terrestrial ecosystem. Studies on processes and mechanisms between different soil respiration components in cropland ecosystem will help to promote greenhouse gases mitigation, carbon sequestration and storage, climate change adaptation, food security and sustainable development of agriculture. In this context, this paper reviews theoretical and technical development of soil respiration component partitioning in recent decades, including the theoretical premises, the development and classification of partitioning techniques. This paper also compares the advantages and disadvantages of most partitioning techniques, analyzes their applicability for different systems, and summarizes the application of current soil respiration partitioning techniques in cropland ecosystem in China as well as in world. Generally speaking, since most soil respiration partitioning techniques are developed in the studies of forest ecosystem, they are not suitable for cropland ecosystem. Presently, isotope methods, root excised method and regression method are major applied techniques of soil respiration partitioning in cropland ecosystem. Due to the differences in theoretical premises and techniques choices for soil respiration partitioning, results from different researches are difficult to compare. Further development of partitioning techniques relies on the breakthrough of soil respiration partitioning theories and future breakthroughs in the researches related to soil respiration partitioning will lie in the following aspects: (1) Component integration methods and root excised method will be improved to better apply in cropland ecosystem by making best of present soil respiration monitoring technologies and systems. The simultaneously monitoring of soil respiration components and environmental factors will be strengthened and cropland carbon budget will be more physically-based and reliable. (2) Large-scale modeling on carbon cycling will take advantages of existing long-term on-site monitoring data to upscale soil respiration processes and mechanisms. With this, existing global carbon cycling models could improve or change current carbon-nitrogen processes and adopt important soil respiration processes. (3) With FACE (free-air carbon dioxide enrichment) experiments, researchers could evaluate the impacts of climate change on different soil respiration components and identify the adaptive mechanisms of soil-plant carbon cycling to climate change. (4) The interaction between soil respiration components and plant-soil-nutrient will be examined to evaluate the integrated impacts of cropland management practices. 参考文献 相似文献 引证文献

Comparison of two respiration monitoring systems for 4D imaging with a Siemens CT using a new dynamic breathing phantom

Four-dimensional computed tomography (4D-CT) requires breathing information from the patient, and for this, several systems are available. Testing of these systems, under realistic conditions, requires a phantom with a moving target and an expandable outer contour. An anthropomorphic phantom was developed to simulate patient breathing as well as lung tumor motion. Using the phantom, an optical camera system (GateCT) and a pressure sensor (AZ-733V) were simultaneously operated, and 4D-CTs were reconstructed with a Siemens CT using the provided local-amplitude-based sorting algorithm. The comparison of the tumor trajectories of both systems revealed discrepancies up to 9.7 mm. Breathing signal differences, such as baseline drift, temporal resolution and noise level were shown not to be the reason for this. Instead, the variability of the sampling interval and the accuracy of the sampling rate value written on the header of the GateCT-signal file were identified as the cause. Interpolation to regular sampling intervals and correction of the sampling rate to the actual value removed the observed discrepancies. Consistently, the introduction of sampling interval variability and inaccurate sampling rate values into the header of the AZ-733V file distorted the tumor trajectory for this system. These results underline the importance of testing new equipment thoroughly, especially if components of different manufacturers are combined.