BIOTEX-biosensing textiles for personalised healthcare management.

Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

Ear-Lead Multiple Smart Bio Sensor System in M-health

In this paper, we present a multi-parameter wearable sensor system within a smartphone to enable a real-time unobtrusive monitoring of core body temperature, electrocardiogram (ear-lead ECG), and blood oxygen saturation (SpO2) on ambulatory patients. Clinical researches illustrating that continuing accurate measurements of core body temperature are crucial to investigate human thermoregulation in ambulatory environment and during activity. On the other part, ECG remains the mainstay test for primary diagnosis and survival analysis of the heart diseases. We implement a wireless multisensory system that measures both the tympanic temperature inside the ear and ECG data from an ear and an arm. The behind-the-Ear device makes desirable to measure ECG data without any chest belt and allow patient move around freely. The proposed Bluetooth device has similarity the form factor of a hearing aid and is wirelessly connected to a smartphone for data transmission and displaying. This device not only gives the doctors access to the core temperature and ECG data in real time, but the device can be controlled – removed and reapplied – by the patient at any time, thus increasing the performance of personal health applications

Evaluation of a Behind-the-Ear ECG Device for Smartphone based Integrated Multiple Smart Sensor System in Health Applications

In this paper, we present a wireless Multiple Smart Sensor System (MSSS) in conjunction with a smartphone to enable an unobtrusive monitoring of electrocardiogram (ear-lead ECG) integrated with multiple sensor system which includes core body temperature and blood oxygen saturation (SpO2) for ambulatory patients. The proposed behind-the-ear device makes the system desirable to measure ECG data: technically less complex, physically attached to non-hair regions, hence more suitable for long term use, and user friendly as no need to undress the top garment. The proposed smart sensor device is similar to the hearing aid device and is wirelessly connected to a smartphone for physiological data transmission and displaying. This device not only gives access to the core temperature and ECG from the ear, but also the device can be controlled (removed and reapplied) by the patient at any time, thus increasing the usability of personal healthcare applications. A number of combination ECG electrodes, which are based on the area of the electrode and dry/non-dry nature of the surface of the electrodes are tested at various locations near behind the ear. The best ECG electrode is then chosen based on the Signal-to-Noise Ratio (SNR) of the measured ECG signals. These electrodes showed acceptable SNR ratio of ~20 db, which is comparable with existing tradition ECG electrodes. The developed ECG electrode systems is then integrated with commercially available PPG sensor (Amperor pulse oximeter) and core body temperature sensor (MLX90614) using a specialized micro controller (Arduino UNO) and the results monitored using a newly developed smartphone (android) application

Wearable continuous vital sign monitoring for deterioration detection and clinical outcomes in hospitalised patients

 Current practice uses physiological early warning scoring (EWS) systems to monitor “standard” vital signs, including heart rate (HR), respiratory rate (RR), blood pressure (BP), oxygen saturations (SpO2) and temperature, coupled with a graded response such as referral for a senior review or increasing monitoring frequency. Early detection of the deteriorating patient is a known challenge within hospital environments, as EWS is dependent on correct frequency of physiological observations tailored to specific patient needs, that can be time consuming for healthcare professionals, resulting in missed or incomplete observations. Wearable monitoring systems (WMS) may bring the potential to fill the gap in vital sign monitoring between traditional intermittent manual measurements and continuous automatic monitoring. However, evidence on the feasibility and impact of WMS implementation remains scarce. The virtual High Dependency Unit (vHDU) project was designed to develop and test the feasibility of deploying a WMS system in the hospital ward environment. This doctoral work aims to critically analyse the roadmap work of the vHDU project, containing ten publications distributed throughout 7 chapters. Chapter 1 (with 3 publications) includes a systematic review and meta-analysis identifying the lack of statistical evidence of the impact of WMS in early deterioration detection and associated clinical outcomes, highlighting the need for high-quality randomised controlled trials (RCTs). It also supports the use of WMS as a complement, and not a substitute, for standard and direct care. Chapter 2 explores clinical staff and patient perceptions of current vital sign monitoring practices, as well as their early thoughts on the use of WMS in the hospital environment through a qualitative interview study. WMS were seen positively by both clinical and patient groups as a potential tool to bridge the gap between manual observations and the traditional wired continuous automatic systems, as long as it does not add more noise to the wards nor replaces direct contact from the clinical staff. In chapter 3, the wearability of 7 commercially available wearables (monitoring HR, RR and SpO2) was assessed, advocating for the use of pulse oximeters without a fingertip probe and a small chest patch to improve worn times from the patients. Out of these, five devices were submitted to measurement accuracy testing (chapter 4, with 3 publications) under movement and controlled hypoxaemia, resulting in the validation of a chest patch (monitoring HR and RR) and proving the diagnostic accuracy of 3 pulse oximeters (monitoring pulse rate, PR and SpO2) under test. These results were timely for the final selection of the devices to be integrated in our WMS, namely vHDU system, explored in chapter 5, outlining the process for its development and rapid deployment in COVID-19 isolation wards in our local hospital during the pandemic. This work is now converging in the design of a feasibility RCT to test the impact of the vHDU system (now augmented with blood pressure and temperature monitoring, completing all 5 vital signs) versus standard care in an unbiased environment (chapter 6). This will also ascertain the feasibility for a multicentre RCT, that may in the future, contribute with the much-needed statistical evidence to my systematic review and meta-analysis research question, highlighted in chapter 1. Finally, chapter 7 includes a critical reflection of the vHDU project and overall doctoral work, as well as its contributions to the field of wearable monitoring.<p class="MsoNormal"/

Wearable devices and IoT applications for symptom detection, infection tracking, and diffusion containment of the COVID-19 pandemic: a survey

Until a safe and effective vaccine to fight the SARS-CoV-2 virus is developed and available for the global population, preventive measures, such as wearable tracking and monitoring systems supported by Internet of Things (IoT) infrastructures, are valuable tools for containing the pandemic. In this review paper we analyze innovative wearable systems for limiting the virus spread, early detection of the first symptoms of the coronavirus disease COVID-19 infection, and remote monitoring of the health conditions of infected patients during the quarantine. The attention is focused on systems allowing quick user screening through ready-to-use hardware and software components. Such sensor-based systems monitor the principal vital signs, detect symptoms related to COVID-19 early, and alert patients and medical staff. Novel wearable devices for complying with social distancing rules and limiting interpersonal contagion (such as smart masks) are investigated and analyzed. In addition, an overview of implantable devices for monitoring the effects of COVID-19 on the cardiovascular system is presented. Then we report an overview of tracing strategies and technologies for containing the COVID-19 pandemic based on IoT technologies, wearable devices, and cloud computing. In detail, we demonstrate the potential of radio frequency based signal technology, including Bluetooth Low Energy (BLE), Wi-Fi, and radio frequency identification (RFID), often combined with Apps and cloud technology. Finally, critical analysis and comparisons of the different discussed solutions are presented, highlighting their potential and providing new insights for developing innovative tools for facing future pandemics

A Multi-Source Harvesting System Applied to Sensor-Based Smart Garments for Monitoring Workers’ Bio-Physical Parameters in Harsh Environments

This paper describes the development and characterization of a smart garment for monitoring the environmental and biophysical parameters of the user wearing it; the wearable application is focused on the control to workers’ conditions in dangerous workplaces in order to prevent or reduce the consequences of accidents. The smart jacket includes flexible solar panels, thermoelectric generators and flexible piezoelectric harvesters to scavenge energy from the human body, thus ensuring the energy autonomy of the employed sensors and electronic boards. The hardware and firmware optimization allowed the correct interfacing of the heart rate and SpO2 sensor, accelerometers, temperature and electrochemical gas sensors with a modified Arduino Pro mini board. The latter stores and processes the sensor data and, in the event of abnormal parameters, sends an alarm to a cloud database, allowing company managers to check them via a web app. The characterization of the harvesting subsection has shown that ≈ 265 mW maximum power can be obtained in a real scenario, whereas the power consumption due to the acquisition, processing and BLE data transmission functions determined that a 10 mAh/day charge is required to ensure the device’s proper operation. By charging a 380 mAh Lipo battery in a few hours by means of the harvesting system, an energy autonomy of 23 days was obtained, in the absence of any further energy contribution

Vital signs prediction and early warning score calculation based on continuous monitoring of hospitalised patients using wearable technology

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS