Future Prediction of Close Contacts in IoT-based Contact Tracing System using a New Real-Life Dataset.

Contact tracing is an effective method for mitigating the infectious diseases spread and it played a crucial role in reducing COVID-19 outbreak. Since the pandemic, there has been an increased concern regarding people's health in hospital and office settings, as these limited air exchange spaces provide a conductive medium for virus spread. Various technologies were used to recognize close contacts autonomously, in addition, multiple machine learning attempts were carried out to determine proximity in contact tracing. This study, however, proposes a unique concept in contact tracing: forecasting future close contact prior to occurrence in order to regulate and control it rather than tracking past occurrences. For our research, we constructed a completely new real-life dataset that was collected during the pandemic in a hospital infectious ward (Alfred Hospital, Melbourne, Australia) utilizing a Bluetooth Low Energy (BLE) Internet of Things (IoT) system. Our prediction technique considers two types of environments: single transceiver environments and multiple transceivers settings, these transceivers record the nearby tags' BLE received signal strength indicator (RSSI) values. The system employs mathematical models and supervised machine learning (ML) algorithms to solve regression and classification problems for workers' pattern recognition within the environment. The output is compared using different metrics, such as efficiency, which reached more than 80%, root mean square errors and mean absolute errors which were as low as 2.4 and 1.2 respectively in some models.

Comparing Efficiency and Performance of IoT BLE and RFID-Based Systems for Achieving Contract Tracing to Monitor Infection Spread among Hospital and Office Staff.

COVID-19 is highly contagious and spreads rapidly; it can be transmitted through coughing or contact with virus-contaminated hands, surfaces, or objects. The virus spreads faster indoors and in crowded places; therefore, there is a huge demand for contact tracing applications in indoor environments, such as hospitals and offices, in order to measure personnel proximity while placing as little load on them as possible. Contact tracing is a vital step in controlling and restricting pandemic spread; however, traditional contact tracing is time-consuming, exhausting, and ineffective. As a result, more research and application of smart digital contact tracing is necessary. As the Internet of Things (IoT) and wearable sensor device studies have grown in popularity, this work has been based on the practicality and successful implementation of Bluetooth low energy (BLE) and radio frequency identification (RFID) IoT based wireless systems for achieving contact tracing. Our study presents autonomous, low-cost, long-battery-life wireless sensing systems for contact tracing applications in hospital/office environments; these systems are developed with off-the-shelf components and do not rely on end user participation in order to prevent any inconvenience. Performance evaluation of the two implemented systems is carried out under various real practical settings and scenarios; these two implemented centralised IoT contact tracing devices were tested and compared demonstrating their efficiency results.

Emerging Technologies Used in Health Management and Efficiency Improvement During Different Contact Tracing Phases Against COVID-19 Pandemic.

Confronted with the COVID-19 health crisis, the year 2020 represented a turning point for the entire world. It paved the way for health-care systems to reaffirm their foundations by using different technologies such as sensors, wearables, mobile applications, drones, robots, Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT). A lot of domains have been renovated such as diagnosis, treatment, and monitoring, as well as previously unprecedented domains such as contact tracing. Contact tracing, in conjunction with the emergence, spread, and public compliance for vaccines, was a critical step for controlling and limiting the spread of the pandemic. Traditional contact tracing is usually dependent on individuals ability to recall their interactions, which is challenging and yet not effective. Consequently, further development and usage of automated, privacy-preserving, digital contact-tracing was required. As the pandemic is coming to an end, it is vital to collect and learn the effective used technologies that aided in fighting the virus in order to be prepared for any future pandemics and to be aware of any literature gaps that must be filled. This paper surveys state-of-the-art architectures, platforms, and applications combating COVID-19 at each phase of the five basic contact tracing phases, including case identification, contacts identification and rapid exposure notification, surveillance, regular follow up and prevention. In addition, there is a phase of preparation and post-pandemic services for current and needed future technology that will aid in the fight against any incoming infectious diseases.

A Wearable Bioimpedance Chest Patch for Real-Time Ambulatory Respiratory Monitoring.

OBJECTIVE: This paper aims to introduce a wearable solution and a low-complexity algorithm for real-time continuous ambulatory respiratory monitoring. METHODS: A wearable chest patch is designed using a bioimpedance (BioZ) sensor to measure the changes in chest impedance caused by breathing. Besides, a medical-grade infrared temperature sensor is utilized to monitor body temperature. The computing algorithm implemented on the patch enables computation of breath-by-breath respiratory rate and chest temperature in real-time. Two wireless communication protocols are included in the system, namely Bluetooth and Long Range (LoRa), which enable both short-range and long-range data transmission. RESULTS: The breathing rate measured in static (i.e., standing, sitting, supine, and lateral lying) and dynamic (i.e., walking, running, and cycling) positions by our device yielded an accuracy of more than 97.8% and 98.5% relative to the ground truth, respectively. Additionally, the device's performance is evaluated in real-world scenarios both indoors and outdoors. CONCLUSION: The proposed system is capable of measuring breathing rate throughout a variety of daily activities. To the best of our knowledge, this is the first BioZ-based wearable patch capable of detecting breath-by-breath respiratory rate in real-time remotely under unrestricted ambulatory conditions. SIGNIFICANCE: This study establishes a strategy for continuous respiratory monitoring that could aid in the early detection of cardiopulmonary disorders in everyday life.

Feasibility of Bluetooth Low Energy wearable tags to quantify healthcare worker proximity networks and patient close contact: A pilot study.

BACKGROUND: The hospital environment is characterised by a dense network of interactions between healthcare workers (HCWs) and patients. As highlighted by the coronavirus pandemic, this represents a risk for disease transmission and a challenge for contact tracing. We aimed to develop and pilot an automated system to address this challenge and describe contacts between HCWs and patients. METHODS: We developed a bespoke Bluetooth Low Energy (BLE) system for the hospital environment with anonymous tags worn by HCWs and fixed receivers at patient room doors. Proximity between wearable tags inferred contact between HCWs. Tag-receiver interactions inferred patient room entry and exit by HCWs. We performed a pilot study in four negative pressure isolation rooms from 13 April to 18 April 2021. Nursing and medical staff who consented to participate were able to collect one of ten wearable BLE tags during their shift. RESULTS: Over the four days, when divided by shift times, 27 nursing tags and 3 medical tags were monitored. We recorded 332 nurse-nurse interactions, for a median duration of 58 s [interquartile range (IQR): 39-101]. We recorded 45 nursing patient room entries, for a median 7 min [IQR: 3-21] of patient close contact. Patient close contact was shorter in rooms on airborne precautions, compared to those not o transmission-based precautions. CONCLUSION: This pilot study supported the functionality of this approach to quantify HCW proximity networks and patient close contact. With further refinements, the system could be scaled-up to support contact tracing in high-risk environments.

A Wearable Bioimpedance Chest Patch for IoHT-Connected Respiration Monitoring.

This paper presents a wearable sensor patch with real-time respiration monitoring by measuring the change in thoracic impedance resulting from breathing. A bioimpedance (BioZ) sensor with two sensing electrodes is employed to measure the chest impedance. In addition, a medical-grade infrared temperature sensor is utilized to detect body temperature. The recorded data is transmitted via a Bluetooth module to a computer for online data computation and waveform visualization. The breath-by-breath breathing rate is calculated using the time difference between two BioZ signal peaks, and the results are validated against a commercial respiration monitoring belt. Experimental tests have been conducted on five subjects in both static (i.e., sitting, supine, sleeping on the left side, sleeping on the right side, and standing) and dynamic (i.e., walking) conditions. The experiment measurements show that the BioZ sensor patch can be used to monitor the breathing rate accurately in static conditions with a low mean absolute error (MAE) of 0.71 breath-per-minute (bpm) and can detect breathing rate effectively in a dynamic environment as well. The results suggest the feasibility of using the proposed approach for respiration monitoring in daily life.

Landmark-Assisted Compensation of User's Body Shadowing on RSSI for Improved Indoor Localisation with Chest-Mounted Wearable Device.

Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user's body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user's body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm.

Soft gold nanowire sponge antenna for battery-free wireless pressure sensors.

The past decade has witnessed growing interest in developing soft wearable pressure sensors with the ultimate goal of transforming today's hospital-centered diagnosis to tomorrow's patient-centered bio-diagnosis. In this context, battery-free wireless antenna-based pressure sensors will be highly advantageous for ubiquitous real-time health monitoring. However, current wireless antennas are largely based on thin films from traditional bulk metallic films or novel nanomaterials with an air-cavity design, which can only be operated in a limited pressure range due to the rigidity of active films and/or inherent cavity dimensions. Herein we report a soft battery-free wireless pressure sensor that is based on a three-dimensional (3D) porous gold nanowire foam-elastomer composite and is fabricated by solution-based conformal electroless plating technology, followed by elastomer encapsulation. We observe a transducer trade-off point for our foam antenna, below which the inductive effect and capacitive effect function together and above which the capacitive effect dominates. When an external pressure is applied, initially the inductance and capacitance increase simultaneously but the capacitance decreases afterwards. This can be transformed into a variable resonant frequency that first decreases linearly and then increases (in the capacitance domination pressure range). Importantly, the linear detection range of the sensor can be tuned simply by adjusting the thickness of the sponge or the rigidity of the elastomer (PDMS). We can achieve a wide pressure range of 0-248 kPa, which is the largest linear detection range reported in the literature (typically from 0 to 30 kPa) to the best of our knowledge. As a proof of concept, we further demonstrated that our gold nanowire foam sensor can be used to weigh people under both static and dynamic conditions.

Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors.

Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.

Blood Pressure Estimation Using On-body Continuous Wave Radar and Photoplethysmogram in Various Posture and Exercise Conditions.

The pulse arrival time (PAT), pre-ejection period (PEP) and pulse transit time (PTT) are calculated using on-body continuous wave radar (CWR), Photoplethysmogram (PPG) and Electrocardiogram (ECG) sensors for wearable continuous systolic blood pressure (SBP) measurements. The CWR and PPG sensors are placed on the sternum and left earlobe respectively. This paper presents a signal processing method based on wavelet transform and adaptive filtering to remove noise from CWR signals. Experimental data are collected from 43 subjects in various static postures and 26 subjects doing 6 different exercise tasks. Two mathematical models are used to calculate SBPs from PTTs/PATs. For 38 subjects participating in posture tasks, the best cumulative error percentage (CEP) is 92.28% and for 21 subjects participating in exercise tasks, the best CEP is 82.61%. The results show the proposed method is promising in estimating SBP using PTT. Additionally, removing PEP from PAT leads to improving results by around 9%. The CWR sensors present a low-power, continuous and potentially wearable system with minimal body contact to monitor aortic valve mechanical activities directly. Results of this study, of wearable radar sensors, demonstrate the potential superiority of CWR-based PEP extraction for various medical monitoring applications, including BP measurement.

A Wireless Wearable Sensor Patch for the Real-Time Estimation of Continuous Beat-to-Beat Blood Pressure.

This paper proposes a wireless wearable sensor system for the continuous beat-to-beat blood pressure (BP) monitoring. The real-time BP can be estimated utilising a 2-parameter regression model based on the pulse arrival time (PAT) and heart rate (HR). The PAT is defined as the time interval between the electrocardiogram (ECG) R-peak and the corresponding maximum inclination point of photoplethysmography (PPG) signal. A wireless wearable sensor patch designed to be attached to the subject's chest is used for the measurement of ECG and PPG signals. The sensor data are transmitted through a Bluetooth low energy (BLE) module to a computer for the real-time online estimation of BP. To verify the feasibility and performance of the proposed system, a 5-day period experiment is conducted on two healthy male subjects for the training and validation of the BP estimation model. On each day, there are two 15 minutes offline sessions for data collection from the sensor patch, which are compared with the reference BP to calibrate the estimation model parameters. After that, a 10 minutes online session is carried out to validate the regression model against the reference BP device. Eventually, the 5-day period data are combined together for an overall BP estimation model, which has good correlation (r=0.82) with the reference BP measurements. The experimental results show the proposed sensor patch with the BP estimation model is capable of the online real-time BP monitoring after an initial calibration procedure.

An Internet-of-Things (IoT) Network System for Connected Safety and Health Monitoring Applications.

This paper presents a hybrid wearable sensor network system towards the Internet of Things (IoT) connected safety and health monitoring applications. The system is aimed at improving safety in the outdoor workplace. The proposed system consists of a wearable body area network (WBAN) to collect user data and a low-power wide-area network (LPWAN) to connect the WBAN with the Internet. The wearable sensors in the WBAN are exerted to measure the environmental conditions around the subject using a Safe Node and monitor the vital signs of the subject using a Health Node. A standalone local server (gateway), which can process the raw sensor signals, display the environmental and physiological data, and trigger an alert if any emergency circumstance is detected, is designed within the proposed network. To connect the gateway with the Internet, an IoT cloud server is implemented to provide more functionalities, such as web monitoring and mobile applications.

Subcutaneous Solar Energy Harvesting for Self-Powered Wireless Implantable Sensor Systems.

This paper presents the study of subcutaneous solar energy harvesting for implantable sensor systems. The characteristics of a flexible solar panel under a 3 mm thick porcine skin are measured under different ambient light conditions. The output power of the solar panel when covered by the skin varies from tens of micro Watts to a few milli Watts depending on the light source. A low-power implantable sensor prototype is proposed to evaluate the performance of the subcutaneous solar energy harvester. It consists of a power management circuit, a temperature sensor and a Bluetooth low energy (BLE) module. The average working current of the prototype is $400 \mu \mathrm {A}$ (transient BLE transmission current is 8 mA), while its sleep current is only $7 \mu \mathrm {A}$. Experimental results show that the subcutaneous solar energy harvester illuminated by both sunlight and artificial light sources can power the implantable prototype.

Integration of Low-Power ASIC and MEMS Sensors for Monitoring Gastrointestinal Tract Using a Wireless Capsule System.

This paper presents a wireless capsule microsystem to detect and monitor the pH, pressure, and temperature of the gastrointestinal tract in real time. This research contributes to the integration of sensors (microfabricated capacitive pH, capacitive pressure, and resistive temperature sensors), frequency modulation and pulse width modulation based interface IC circuits, microcontroller, and transceiver with meandered conformal antenna for the development of a capsule system. The challenges associated with the system miniaturization, higher sensitivity and resolution of sensors, and lower power consumption of interface circuits are addressed. The layout, PCB design, and packaging of a miniaturized wireless capsule, having diameter of 13 mm and length of 28 mm, have successfully been implemented. A data receiver and recorder system is also designed to receive physiological data from the wireless capsule and to send it to a computer for real-time display and recording. Experiments are performed in vitro using a stomach model and minced pork as tissue simulating material. The real-time measurements also validate the suitability of sensors, interface circuits, and meandered antenna for wireless capsule applications.

Systolic Time Interval Estimation Using Continuous Wave Radar With On-Body Antennas.

The estimation of systolic time intervals (STIs) is done using continuous wave (CW) radar at 2.45 GHz with an on-body antenna. MOTIVATION: In the state of the art, typically bioimpedance, heart sounds and/or ultrasound are used to measure STIs. All three methods suffer from insufficient accuracy of STI estimation due to various reasons. CW radar is investigated for its ability to overcome the deficiencies in the state of the art. METHODS: Ten healthy male subjects aged 25-45 were asked to lie down at a 30 incline. Recordings of 60 s were taken without breathing and with paced breathing. Heart sounds, electrocardiogram, respiration, and impedance cardiogram were measured simultaneously as reference. The radar antennas were placed at two positions on the chest. The antennas were placed directly on the body as well as with cotton textile in between. The beat to beat STIs have been determined from the reference signals as well as CW radar signals. RESULTS: The results indicate that CW radar can be used to estimate STIs in ambulatory monitoring. SIGNIFICANCE: The results pave way to a potentially more compact method of estimating STIs, which can be integrated into a wearable device.

Development of a wearable plantar force measurement device for gait analysis in remote conditions.

The pressure field that exists between the foot and the supporting surface is identified as the foot plantar pressure. The information obtained from foot plantar pressure measurements has useful applications that include diagnosis of gait disturbances, optimization of footwear design, sport biomechanics and prevention of injury. Using wearable technology to measure foot plantar pressure continuously allows the collection of comprehensive real-life data sets while interfering minimally with the subject's daily activities. This paper presents the design of a wearable device to measure foot plantar pressure. Mechanical and electrical design considerations as well as data analysis are discussed. A pilot study involving 20 physically fit volunteers (15 males and 5 females, ageing from 20 - 45) performing a variety of physical activities (such as standing, walking, jumping and climbing up and down stairs) illustrate the potential of the device in terms of its wearability, and suitability for unobtrusive long-term monitoring.

Comparison of the impedance cardiogram with continuous wave radar using body-contact antennas.

This paper describes a continuous wave (CW) radar system with body-contact antennas and basic signal processing. The goal is to assess the signals' reproducibility across different subjects as well as a respiration cycle. Radar signals using body-contact antennas with a carrier frequency of 868 MHz are used to acquire the cardiac activity at the sternum. The radar I and Q channel signals are combined to form their magnitude. Signals are collected from six healthy males during paced breathing conditions. The electrocardiogram (ECG) and impedance cardiogram (ICG) signals are acquired simultaneously as reference. The chosen feature in the radar signal is the maximum of its second derivative, which is closest to the ICG B-point. The median and mean absolute errors in pre-ejection period (PEP) in milliseconds between the ICG's B-point and chosen feature in the radar signal range from -6-119.7 ms and 7.8-62.3 ms for all subjects. The results indicate that a reproducible radar signal is obtained from all six subjects. More work is needed on understanding the origin of the radar signals using ultrasound as a comparison.

Flexible wearable sensor nodes with solar energy harvesting.

Wearable sensor nodes have gained a lot of attention during the past few years as they can monitor and record people's physical parameters in real time. Wearable sensor nodes can promote healthy lifestyles and prevent the occurrence of potential illness or injuries. This paper presents a flexible wearable sensor system powered by an efficient solar energy harvesting technique. It can measure the subject's heartbeats using a photoplethysmography (PPG) sensor and perform activity monitoring using an accelerometer. The solar energy harvester adopts an output current based maximum power point tracking (MPPT) algorithm, which controls the solar panel to operate within its high output power range. The power consumption of the flexible sensor nodes has been investigated under different operation conditions. Experimental results demonstrate that wearable sensor nodes can work for more than 12 hours when they are powered by the solar energy harvester for 3 hours in the bright sunlight.

A Doppler Radar System for Sensing Physiological Parameters in Walking and Standing Positions.

Doppler radar can be implemented for sensing physiological parameters wirelessly at a distance. Detecting respiration rate, an important human body parameter, is essential in a range of applications like emergency and military healthcare environments, and Doppler radar records actual chest motion. One challenge in using Doppler radar is being able to monitor several patients simultaneously and in different situations like standing, walking, or lying. This paper presents a complete transmitter-receiver Doppler radar system, which uses a 4 GHz continuous wave radar signal transmission and receiving system, to extract base-band data from a phase-shifted signal. This work reports experimental evaluations of the system for one and two subjects in various standing and walking positions. It provides a detailed signal analysis of various breathing rates of these two subjects simultaneously. These results will be useful in future medical monitoring applications.

Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System.

Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more lowpower sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting.