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.

Combination of Chemotherapy and Mild Hyperthermia Using Targeted Nanoparticles: A Potential Treatment Modality for Breast Cancer.

Despite the clinical benefits that chemotherapeutics has had on the treatment of breast cancer, drug resistance remains one of the main obstacles to curative cancer therapy. Nanomedicines allow therapeutics to be more targeted and effective, resulting in enhanced treatment success, reduced side effects, and the possibility of minimising drug resistance by the co-delivery of therapeutic agents. Porous silicon nanoparticles (pSiNPs) have been established as efficient vectors for drug delivery. Their high surface area makes them an ideal carrier for the administration of multiple therapeutics, providing the means to apply multiple attacks to the tumour. Moreover, immobilising targeting ligands on the pSiNP surface helps direct them selectively to cancer cells, thereby reducing harm to normal tissues. Here, we engineered breast cancer-targeted pSiNPs co-loaded with an anticancer drug and gold nanoclusters (AuNCs). AuNCs have the capacity to induce hyperthermia when exposed to a radiofrequency field. Using monolayer and 3D cell cultures, we demonstrate that the cell-killing efficacy of combined hyperthermia and chemotherapy via targeted pSiNPs is 1.5-fold higher than applying monotherapy and 3.5-fold higher compared to using a nontargeted system with combined therapeutics. The results not only demonstrate targeted pSiNPs as a successful nanocarrier for combination therapy but also confirm it as a versatile platform with the potential to be used for personalised medicine.

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.

Analysis of Spectral Estimation Algorithms for Accurate Heart Rate and Respiration Rate Estimation Using an Ultra-Wideband Radar Sensor.

Non-contact vital sign monitoring has been an important research topic recently due to the ability to monitor patients for an extended period especially during sleep without requiring uncomfortable attachments. Radar is a popular sensor for vital sign monitoring research. Various algorithms have been proposed for estimating respiration rate and heart rate from the radar data. But many algorithms rely on Fast Fourier Transform (FFT) to convert time domain signal to the frequency domain and estimate vital signs, despite FFT having limitation of frequency resolution being inverse of the time interval of data sample. However, there are other spectral estimation algorithms, which have not been much researched into the suitability of vital sign estimation using radar signals. In this paper, we compared eight different types of spectral estimation algorithms, including FFT, for respiration rate and heart rate estimation of stationary subjects in a controlled environment. The evaluation is based on extensive data consisting of different stationary subject positions. Considering the results, the eligibility of algorithms other than FFT for respiration rate and heart rate estimation is demonstrated. Using this work, researchers can get an overview on which algorithm is suitable for their work without the need to review individual algorithms separately.

Flexible Forearm Triboelectric Sensors for Parkinson's Disease Diagnosing and Monitoring.

Existing approaches that assess and monitor the severity of Parkinson's Disease (PD) focus on the integration of wearable devices based on inertial sensors (accelerometers, gyroscopes) and electromyographic (EMG) transducers. Nevertheless, some of these sensors are bulky and lack comfortability. This manuscript presents triboelectric nanogenerators (TENGs) as an alternative stretchable sensor solution enabling PD monitoring systems. The prototype has been developed using a triboelectric sensor based on Ecoflex™ and PEDOT:PSS that is placed on the forearm. The movement of the skin above the forearm muscles and tendons correlates with the extension and flexion of fingers and hands. This way, the small gap of 0.5 cm between the polymer layers is displaced, generating voltage due to the triboelectric contact. Signals from preliminary experiments can discriminate different dynamics of emulated tremor and bradykinesia in hands and fingers. A modified version of the TS is integrated with a printed circuit board (PCB) in a single package with signal conditioning and wireless data transmission. The sensor platforms have demonstrated a good sensitivity to PD symptoms like bradykinesia and tremor based on the Unified Parkinson's Disease Rating Scale (MDS:UPDRS).

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.

Next-generation ingestible devices: sensing, locomotion and navigation.

There is significant interest in exploring the human body's internal activities and measuring important parameters to understand, treat and diagnose the digestive system environment and related diseases. Wireless capsule endoscopy (WCE) is widely used for gastrointestinal (GI) tract exploration due to its effectiveness as it provides no pain and is totally tolerated by the patient. Current ingestible sensing technology provides a valuable diagnostic tool to establish a platform for monitoring the physiological and biological activities inside the human body. It is also used for visualizing the GI tract to observe abnormalities by recording the internal cavity while moving. However, the capsule endoscopy is still passive, and there is no successful locomotion method to control its mobility through the whole GI tract. Drug delivery, localization of abnormalities, cost reduction and time consumption are improvements that can be gained from having active ingestible WCEs. In this article, the current technological developments of ingestible devices including sensing, locomotion and navigation are discussed and compared. The main features required to implement next-generation active WCEs are explored. The methods are evaluated in terms of the most important features such as safety, velocity, complexity of design, control, and power consumption.

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.

Chest-based Real-Time Pulse and Respiration Monitoring Based on Bio-Impedance.

Pulse wave and respiration are two important vital signals in diagnosing and treating diseases. In this paper, we investigated a Bio-impedance (BImp) based respiration and pulse wave monitoring system. The BImp signal is successfully extracted from a wearable device placed on the shoulder. Using the rate calculation algorithm, heart rate (HR), and respiration rate (RR) values are extracted accurately. The data is collected during different steps of breathing including slow, fast, deep, hold, and normal from 10 volunteers. The accuracy of HR results is compared to that of extracted from PPG with considering ECG based HR as reference. The extracted RR values are investigated against TCo2 sensor's output. The estimation of both RR and HR extracted from the BImp signal has higher accuracy compared to the other methods.

A Hands-free Human-Computer-Interface Platform for Paralyzed Patients Using a TENG-based Eyelash Motion Sensor.

People with body disabilities as a result of neurological diseases or physical accidents, face daily troubles in some situations that require arm or finger motions. Access to expensive assistive technologies can be difficult, and job opportunities can be low for patients with limited mobility. Triboelectric nanogenerators (TENGs) bring a new concept enabling the design of special sensors that can be used in Human-Computer-Interfaces (HCIs) to support people with disabilities. In this manuscript, it is proposed a novel eye motion sensor based on TENG integrated into an HCI leading to hands-free typing on the computer. We demonstrate that by controlling the cursor, the user can pick up the characters from a virtual keyboard and write an algorithm in the Integrated-Development-Environment (IDE) of Python language. The novel eye sensor recognizes the eyelash motion detected from the triboelectric interaction between human hair and silicone. It is shown that a user is able to write a simple python program to display a message on the computer without the use of hands. Finally, we hope this development can support disabled patients to improve their programming skills and provide improved job opportunities in areas such as information technology or computer science.

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.

An Automatic Navigation and Pressure Monitoring for Guided Insertion Procedure.

Navigation is an important feature needed for medical insertion procedures. It is required to guide the medical device in the right direction at the right time. Navigation techniques used in the Wireless Capsule Endoscopy and conventional endoscopy fields are based on image-guided systems that require a large amount of data to be transferred and processed computationally. These issues increase system complexity as well as the overall system and procedure costs. Moreover, these systems cannot provide the required information in dark or liquid areas. To improve the medical internal inspections capabilities, we present a pressure direction measurement system that can be implemented for a capsule endoscope; ordinary endoscopy; and any other insertion procedure where navigation and safety are required. The system can operate in dark and liquid areas because no visualization is required. The system consists of a pressure sensor placed on a semi-hemisphere on top of the steering device to detect azimuth and polar angle variation according to the direction at any differentiable path.

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.

Antifibrotic Effect of Boric Acid in Rats with Epidural Fibrosis.

BACKGROUND: Epidural fibrosis is a major problem after spine surgery, with some patients having recurrent symptoms secondary to excessive formation of scar tissue resulting in neurologic compression. We used a rat laminectomy model to determine if topical application of boric acid could be helpful in the prevention of epidural fibrosis. METHODS: Rats were randomly assigned to 2 control and 2 experimental groups (n = 8 for each group). The negative control group received no surgery, and the positive control group underwent laminectomy only. Experimental groups were classified according to the study agents applied onto the dura mater after laminectomy at the L3 level: 2.5% boric acid solution and 5% boric acid solution. The extent of epidural fibrosis was assessed 4 weeks later macroscopically and histopathologically. RESULTS: Boric acid reduced epidural fibrosis in rats after laminectomy. The effect of 5% boric acid solution was more pronounced (P < 0.05) compared with the 2.5% solution. CONCLUSIONS: The antifibrotic effect of boric acid solution for the prevention of epidural fibrosis suggests that boric acid should be further evaluated in future studies for the prevention of epidural fibrosis.

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.