Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing

In this paper, a first approach to the design of a portable device for non-contact monitoring of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation exercises at home. To provide an extensible solution to the remote monitoring using this sensor and other devices, the design and preliminary development of an e-Health platform based on the Internet of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution, two quasi-experimental studies have been developed, comparing the estimations with respect to the golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error, the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm), 0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values were -0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period show the technical and functional feasibility of the prototype and serve as a preliminary validation of the device for respiratory rate monitoring of patients with COPD.Ministerio de Ciencia e Innovación PI15/00306Ministerio de Ciencia e Innovación DTS15/00195Junta de Andalucía PI-0010-2013Junta de Andalucía PI-0041-2014Junta de Andalucía PIN-0394-201

Optimization of the position of single-lead wireless sensor with low electrodes separation distance for ECG-derived respiration

A classical method for estimation of respiratory information from electrocardiogram (ECG), called ECG - derived respiration (EDR), is using flexible electrodes located at standard electrocardiography positions. This work introduces an alternative approach suitable for miniaturized sensors with low inter-electrode separation and electrodes fixed to the sensor encapsulation. Application of amplitude EDR algorithm on single-lead wireless sensor system with optimized electrode positions shows results comparable with standard robust systems. The modified method can be applied in daily physiological monitoring, in sleep studies or implemented in smart clothes when standard respiration techniques are not suitable

Sensing Movement: Microsensors for Body Motion Measurement

Recognition of body posture and motion is an important physiological function that can keep the body in balance. Man-made motion sensors have also been widely applied for a broad array of biomedical applications including diagnosis of balance disorders and evaluation of energy expenditure. This paper reviews the state-of-the-art sensing components utilized for body motion measurement. The anatomy and working principles of a natural body motion sensor, the human vestibular system, are first described. Various man-made inertial sensors are then elaborated based on their distinctive sensing mechanisms. In particular, both the conventional solid-state motion sensors and the emerging non solid-state motion sensors are depicted. With their lower cost and increased intelligence, man-made motion sensors are expected to play an increasingly important role in biomedical systems for basic research as well as clinical diagnostics

Design of a Non-Dispersive Infra-Red (NDIR) based CO2 sensor to detect the human respiratory CO2

Respiratory Carbon dioxide (CO2) contains substantial amount of information that can be used to diagnose and treat pulmonary diseases. Many devices have been developed for this purpose, such as capnography, vital monitor, peak flow meter, spirometer etc. There are many CO2 sensor are available in the market but among them NDIR based sensors are considered to be most inexpensive with its accuracy in terms of sensitivity and fast response time. There are commonly two types of technology available for detection; mainstream and sidestream. Mainstream technology is preferable than sidestream because sidestream is not applicable in intubated patients and at the same time it tends to give delay in detection due to longer transmission tube. Most of the NDIR CO2 sensor are being used for the environmental CO2 detection and there are very few mainstream NDIR based CO2 sensor are available in the market. These sensor have a vast number of advantages with some disadvantages as well; such as high response time, thermal noise, temperature increase and others. This project proposed the specification of the electrical circuit of the NDIR CO2 sensor combined with a gas chamber to detect human respiratory CO2. To determine the specification of the CO2 sensor circuit, the components value has been calculated and then the circuit design has been carried out by using Multisim Software. The overall CO2 sensor circuit has six circuit blocks named oscillator, driver circuit, preamplifier, voltage regulator, rectifier, LPF and each of the blocks were built and simulated in the Multisim software. After the simulation the circuit has been built on breadboard to test the output. An IR source from International Light Technologies (ILT) 4115-2A and pyroelectric photodetector L2100X2020 from laser component were used for this project as NDIR components. After the successful simulation from breadboard a gas acquisition cell has been designed to acquire the human CO2 gas. The design has been done by using Solid Works software and printed from a 3D printing machine. The material used for this chamber was ABS. After placing all the calculated components with the source and detector the output has been observed on the digital oscilloscope as a capnograph wave form showing the voltage range. These waveforms are being used in a capnometer determining respiratory diseases. The circuit shows a response time of 6 second with less noise and the waveform showed clear view of detected CO2 without any temperature increase

Effectively Measuring Respiratory Flow with Portable Pressure Data using Back Propagation Neural Network

Continuous respiratory monitoring is an important tool for clinical monitoring. The most widely used flow measure device is nasal cannulae connected to a pressure transducer. However, most of these devices are not easy to carry and continue working in uncontrolled environments is also a problem. For portable breathing equipment, due to the volume limit, the pressure signals acquired by using the airway tube may be too weak and contain some noisy, leading to huge errors in respiratory flow measures. In this paper, a cost-effective portable pressure sensor based respiratory measure device is designed. This device has a new airway tube design, which enables the pressure drop efficiently after the air flowing through the airway tube. Also, a new back propagation (BP) neural network based algorithm is proposed to stablise the device calibration and remove pressure signal nosie. For improving the reability and accuracy of proposed respiratory device, a through experimental evaluation and a case study of proposed BP neural network algorithm have been carried out. The results show that giving proper parameters setting, the proposed BP neural network algorithm is capable of efficiently improving the reliability of new designed respiratory device

Electronic Noses for Biomedical Applications and Environmental Monitoring

This book, titled “Electronic Noses for Biomedical Applications and Environmental Monitoring”, includes original research works and reviews concerning the use of electronic nose technology in two of the more useful and interesting fields related to chemical compounds detection of gases. Authors have explained their latest research work, including different gas sensors and materials based on nanotechnology and novel applications of electronic noses for the detection of diverse diseases. Some reviews related to disease detection through breath analysis, odor monitoring systems standardization, and seawater quality monitoring are also included

Monitoring, Visualization and Assessment of Air Pollutant Emissions on Construction Sites

The construction industry is always ranked as one of the largest emission contributors of air pollutants including nitrogen oxides (NOx), carbon oxides (CO), volatile organic compounds (VOCs), and sulfur oxides (SOx), which accounts for approximate 23% of the global air pollutions each year. These pollutants are detrimental to the ambient air quality and the health and safety of construction practitioners. The high pollutant emission level has attracted the government’s interests to release regulations and initiatives to reduce the air pollutant emissions of construction projects. Also, construction practitioners and researchers are encouraged to mitigate the environmental impacts during the construction process. So far, most of the mitigation efforts have been placed on pre-assessing the environmental impacts of construction activities in the planning stage using emission estimation models. The emission estimation models were developed based on the emission rate analysis of the uninstalled engines in the laboratory environment. Therefore, the estimation models are not able to reflect the real-world emission rates, especially the emission rates of different working modes. In addition, the Portable Emissions Measurement System (PEMS) is employed to monitor the air pollutant emissions of the operating equipment in the construction stage. However, the costly expenses and the particular precautions when using the PEMS to monitor the air pollutant emissions significantly impede the utilization of PEMS. Also, it is impossible to install PEMS to each piece of construction equipment for the air pollutant emission monitoring of the whole construction projects. The main objective of this research is to develop a set of tools to monitor and visualize the air pollutant emission on construction sites in the real-time and automatic manner. Towards this objective, an Internet of Things (IoT)-based system is created with the integration of microcontrollers, microsensors, and high-definition (HD) cameras. Specifically, the system can be employed to: 1) monitor the onsite air pollutant emissions during construction operations in an automatic and real-time manner; 2) dynamically and continuously visualize the air pollutant emission; 3) automatically trigger alarms when the air pollutant emissions violate the standards; and 4) quantitatively assess the potential impacts on ambient air quality and the health of workforces. The system has been tested on real construction sites. The results indicated that the system could assist construction practitioners in the monitoring and visualization of the air pollutants produced from construction operations. Also, the results are able to facilitate decision-making on reducing the air pollutant emissions and promote the sustainability of construction operations

Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes