Wireless Health Monitoring System Research Articles

Whole-process analysis and implementation of a self-powered wireless health monitoring system for railway bridges: Theory, simulation and experiment

In this work, a self-powered wireless health monitoring system (SP-WHMS) combined with the Internet-of-Things (IoT) is proposed and implemented for a long-term and long-distance health monitoring of railway bridges. This system frees the wireless sensor nodes (WSNs) from their dependence on chemical batteries by using the piezoelectric energy harvester (PEH) to harvest the vibration energy from bridges to power the WSNs. The whole-process of the SP-WHMS including energy conversion, energy storage, energy management and application is analyzed at the first time through theory, simulation and experiment. For the energy conversion, a reported PEH model with high energy conversion efficiency (called D-M PEH) is refabricated, which can achieve a maximum average output power of 0.9 W under unit harmonic acceleration excitation. For the energy storage, a high-precision iteration analysis procedure (IAP) is proposed to predict the charging process of the strong-coupled PEHs and the charging process under non-harmonic excitations. The comparison results show that the IAP’s prediction deviates less than 3% from the experiment result and even less than 1% from the simulation result. For the energy management, an AP64500 chip with two adjustable input voltage thresholds and a stable output voltage ensures the normal operation of the SP-WHMS and makes the SP-WHMS suitable for different types of WSNs. For the application, the synergies between energy conversion, storage and management are considered, and the effectiveness of the SP-WHMS used on railway bridges is tested through an activation experiment. This work provides a technical guidance and framework for the implementation of the self-powered wireless monitoring on railway bridges.

The Wireless Health Monitoring for Neonatal Care

Abstract: The wireless health monitoring for neonatal care project addresses the critical need for advanced and non-intrusive health monitoring solutions for newborns in neonatal care settings. Approximately 10% of newborns require assistance at birth, and preterm infants often require specialized stabilization or resuscitation. Traditional monitoring methods, such as electrocardiography (ECG) and pulse oximetry (PO), face challenges in electrode application, delayed HR readings, and potential inaccuracies in assessing cardiac output. This project aims to develop a wireless health monitoring system that overcomes these limitations and offers real-time and accurate monitoring of vital parameters, with a focus on heart rate. The proposed system leverages wireless technology to enable continuous and non-intrusive monitoring, enhancing the efficiency of neonatal care in delivery rooms and neonatal intensive care units (NICUs). By incorporating state-of-the-art sensors and communication protocols, the system aims to provide timely and reliable data on neonatal vital signs. The wireless nature of the solution eliminates concerns related to electrode application difficulties, equipment availability, and the risk of hypothermia.

Shape Evolution of Indium Sulfide Heterostructures via Carbon Nanotube Scrambling: Towards Reliable Sustainability and Mitigating Leakage Current in Supercapacitors

For sustainable energy storage devices with long-term endurance, exploring novel electrode materials can be a realistic focus in the areas of robust structures, surface area control, high channel conductivity, and others. A composite of a hierarchical series of single-walled carbon nanotubes (SWNTs) with In2S3 was synthesized by applying a simple one-step solvothermal method. A SWNT scaffold yields a good conductive pathway, leading to the improved electron transportation and catalytic behaviors. This promotes the robust formation of materials and their enhancement in surface activity and specific capacitance. Herein, the nucleated nanocomposites based on SWNT-mediated In2S3 improve the specific capacitance (1268 F·g−1 at 10 mVs−1) to a remarkable 92.4% of its capacitance even after 10,000 cycles, and furthermore, the robust cocoon-like structure of INS5 (5 mL SWNT doped in In2S3) shows an excellent 97.8% of cyclic retention (10,000 cycles). As a conceptual demonstration of system integration, the as-fabricated symmetric supercapacitor (SSC) device is successfully integrated into the Bluetooth/photoplethysmography (BLE/PPG) module for a wireless sensor network. These findings, through indium sulfides with SWNT scrambling, are expected to contribute to the next-generation solid-state-supercapacitor (SSC)-integrated module in the wireless health monitoring system.

Scorpion-inspired dual-bionic, microcrack-assisted wrinkle based laser induced graphene-silver strain sensor with high sensitivity and broad working range for wireless health monitoring system

Scorpion-inspired dual-bionic, microcrack-assisted wrinkle based laser induced graphene-silver strain sensor with high sensitivity and broad working range for wireless health monitoring system

A piezoelectric energy harvester for freight train condition monitoring system with the hybrid nonlinear mechanism

To improve operational safety and system reliability, real-time wireless health monitoring systems are necessary for freight trains. However, due to the lack of onboard power and space restrictions, monitoring sensors rely on batteries as power sources, which are not eco-friendly and involve high maintenance costs for battery replacement. Therefore, developing self-powered maintenance-free wireless monitoring sensors integrated with energy harvesting is in urgent demand. Here, we propose a compact ultralow-frequency and broadband piezoelectric energy harvester (UBPEH) that can be easily installed in the limited space of the axle box to effectively harvest bogie lateral vibrations. The T-shaped UBPEH employs magnetic interaction to soften the stiffness and strengthen the stopper operation in a low-frequency range. To predict and optimize the prototype, we establish the model of UBPEH by considering the displacement, inclination angle, and shape of the magnets. Theoretical and experimental results show that the prototyped UBPEH might operate in the range of 1–11 Hz, covering the representative frequencies of bogie vibrations on freight trains. An output power of 605 μW on a matched resistance of 200 kΩ under the acceleration of 11 Hz and 0.5 g (g = 9.8 m s−2) is achieved, and the harvested electric power can successfully drive typical commercial wireless Bluetooth sensors. Furthermore, the harvester possesses output stability and mechanical durability under actual service hours of freight trains. The results of this work pave the way to implement self-powered wireless condition monitoring on freight trains.

Bio Parameters Monitoring System for Sea Researchers using LiFi

Different types of health monitoring systems are now available in the market. We are using them as a part of our day-to-day life to analyze health conditions. In the case of sea researchers and scuba divers, the medium they are working is water. The health difficulties are more inside the water. So there is a need to develop a health monitoring system for sea researcher’s/scuba divers to analyze their health condition frequently to ensure their safety. The proposed work uses LiFi technology as the communication method to transmit and receive corresponding bio parameter values. This work aims to provide a harmless wireless health monitoring system that will provide maximum efficiency inside the water.

Microcontroller Based Wireless Patient Health Monitoring System

This paper represents the design and simulation of a wireless patient health monitoring system. Generally, in the hospitals where patient’s body temperature needs to be continuously monitored, which is usually done by a doctor or other paramedical staff by continuously observing the temperature, pulse rate, blood pressure and maintaining its record. This is a very monotonous routine and can really be nerve-racking, especially in overcrowded hospitals. The primary function of this system is to monitor the temperature, blood pressure and pulse rate of a patient’s body, and transmit the information wirelessly to the doctor’s office on the LCD display unit. In this advanced system, a transmitting unit constantly scan patient’s body temperature, blood pressure and pulse rate through digital sensors, and shows them on the transmitter LCD. At the receiving end, a receiver is used to collect the data, decode it and feed them to another microcontroller which then displays it on the doctor’s LCD screen. The receiver unit is kept in the doctor’s office to constantly show the patient’s body temperature, blood pressure and pulse rate wirelessly. An alarm is also activated at the receiver end where the doctor will be, and is activated when the patient’s temperature, blood pressure and pulse rate goes below or above the normal human threshold value, which are 37°C, SBP- less than 120 mm Hg, DBP- less than 80mm Hg and 70 beats per minute. An SMS will be sent to the doctor, just in case he or she is outside the area and to present as a path for continuous update. The advanced system was simulated using Proteus software and programs written in Embedded C language. The result achieved shows a systematic method of relating information to the doctor on duty for urgent attention to patients.

Review on Arduino Based Wireless Health Monitoring System for covid-19 Patients

Review on Arduino Based Wireless Health Monitoring System for covid-19 Patients

A low quiescent power wireless rotating machinery condition monitoring system

Vibration-based monitoring of rotating machinery is rapidly evolving within the aerospace industry with priority on detecting impending failures. The workhorse of such monitoring system remains a piezoelectric-based accelerometers which requires a wired-harnesses, connectors, significant power, and signal conditioning, etc. Raytheon Technologies Research Center (RTRC) along with Collins Aerospace and Sandia National Laboratory have jointly developed an Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE). 
 This is a low power alternate for a conventional wired vibration-based monitoring system. This self-contained sensor system includes: (1) a low quiescent power sensing element with a wake-up module, (2) a wireless communication module, and (3) a coin-cell battery. Leveraging work performed under Defense Advanced Research Projects Agency (DARPA) N-Zero program. This wireless health monitoring system can operate in a quiescent low power mode (~10nW) for a period of several years without servicing. With an exceedance above a preset vibration level (at designate characteristic frequencies), the sensor wakes up and wirelessly sends a warning of a precursor-to-failure.
 The ARISE sensor and wake-up module package has been validated with a replicated vibration environment acquired from a selected rotating machinery subject to progressive damage at the Structural Dynamics Laboratory at RTRC. The failure precursor is successfully detected by the sensor which triggers the wake-up module.
 This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA) Micro Technology Office (MTO), under Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE) program.

Design of Wearable Wireless Health Monitoring System and Status Recognition Algorithm

A wearable wireless health monitoring system for drug addicts in compulsory rehabilitation centers was proposed. The system can continuously monitor multiple physiological parameters of drug addicts in real time, and issue early warning information when abnormal physiological parameters occur, so as to play the role of timely medical practice. In addition, this study proposes a convolutional neural network (CNN)model, which can evaluate the health status of drug addicts based on multiple physiological parameters. Experiments show that the model can be applied to the task of body state recognition in the open physiological parameter data set, and the recognition accuracy can reach up to 100% in a single physiological parameter data set; when the whole physiological data set is used, the recognition accuracy can reach 99.1%. The recognition accuracy exceeds the performance of the traditional pattern recognition method on this task, which verifies the superiority of the model.

On the topology design of large wireless sensor networks for distributed consensus with low power consumption

Sensor-based structural health monitoring systems are commonly used to provide real-time information and detect damage in complex structures. In particular, wireless structural health monitoring systems are of low cost but, since wireless sensors are powered with batteries, a low power consumption is critical. A common approach for wireless structural health monitoring is to use a distributed computation strategy, which is usually based on consensus algorithms. Power consumption in such wireless consensus networks depends on the number of connections of the network. If sensors are randomly connected, there is no control on the power consumption. In this article, we present a novel strategy to connect a large number of wireless sensors for distributed consensus with low power consumption by combining small networks with basic topologies using the Kronecker product.

Flow type electromagnetic based energy harvester for pipeline health monitoring system

This work presents a novel flow type electromagnetic based energy harvester for generating the useful electrical power from the flowing fluid in a pipeline. The energy produced by the developed harvester can be utilized to operate wireless sensors nodes used for pipeline health monitoring system. Developed energy harvester has a spherical permanent magnet freely located in a pipe section and a wound coil produced around a plastic pipe. Fluid flow through the harvester causes the magnet to simultaneously float and rotate in the wound coil. This rotation of the magnet induced the voltage in the wound coil. The developed device when applied to an air flow rate of 1.03 l/s generated an open circuit rms voltage of 1 V and a maximum load power of 1456 µW when connected to 290 Ω load. Moreover, when connected to an AC to DC voltage rectifier, the flow energy harvester produced an open circuit potential of 2.5 V DC at the flow rate of 1.03 l/s. Furthermore, at the same flow condition a load DC power of 1190.7 µW is obtained under load resistance of 3 kΩ. Additionally, with the developed harvester a 4 V battery is charged from 0.08 V up to 2.4 V in time duration of 28 min. The power and power density levels produced by the developed energy harvester are significant and are quite enough to operate sensors in wireless health monitoring system of a pipeline.

An Autonomous Wireless Health Monitoring System Based on Heartbeat and Accelerometer Sensors

Falls are a main cause of injury for patients with certain diseases. Patients who wear health monitoring systems can go about daily activities without limitations, thereby enhancing their quality of life. In this paper, patient falls and heart rate were accurately detected and measured using two proposed algorithms. The first algorithm, abnormal heart rate detection (AHRD), improves patient heart rate measurement accuracy and distinguishes between normal and abnormal heart rate functions. The second algorithm, TB-AIC, combines an acceleration threshold and monitoring of patient activity/inactivity functions to accurately detect patient falls. The two algorithms were practically implemented in a proposed autonomous wireless health monitoring system (AWHMS). The AWHMS was implemented based on a GSM module, GPS, microcontroller, heartbeat and accelerometer sensors, and a smartphone. The measurement accuracy of the recorded heart rate was evaluated based on the mean absolute error, Bland–Altman plots, and correlation coefficients. Fourteen types of patient activities were considered (seven types of falling and seven types of daily activities) to determine the fall detection accuracy. The results indicate that the proposed AWHMS succeeded in monitoring the patient’s vital signs, with heart rate measurement and fall detection accuracies of 98.75% and 99.11%, respectively. In addition, the sensitivity and specificity of the fall detection algorithm (both 99.12%) were explored.

Body Heat Powers Future Electronic Skins

Ruoming Tian recently joined the Mark Wainwright Analytical Centre as an XRD research assistant at the University of New South Wales. She was formerly a Research Scientist at the Toyota Physical and Chemical Research Institute in Japan (2015–2018) and received her PhD from the University of New South Wales in 2014. Her research interests focus on the nano- and micro-materials, flexible hybrid materials, and smart devices for harvesting both high- and low-grade thermal energy. She has published 2 book chapters and 18 peer-reviewed articles and delivered more than 15 talks at international conferences and leading research institutes. Yuqing Liu is currently an Associate Research Fellow and received her PhD (2018) in the Intelligent Polymer Research Institute (IPRI), University of Wollongong, Australia. Her research interest is on the fabrication of wearable energy storage and conversion devices (e.g. thermos-cells, batteries, supercapacitors, etc.), mainly focusing on nanostructured electrode materials, gel electrolyte/electrode interface, and device fabrication via printing techniques. Kunihito Koumoto, Professor Emeritus of Nagoya University, received his BS, MS, and PhD from the University of Tokyo (UT). He served as an Assistant Professor, Lecturer, and Associate Professor at UT (1979–1992) and a full professor at Nagoya University (1992–2015). He has published 495 papers and reviews and 58 book chapters in inorganic materials chemistry. He was awarded Medal with Purple Ribbon by the Emperor of Japan in 2013 and Outstanding Achievement Award by the International Thermoelectric Society in 2018. His current research focuses on inorganic/organic nanohybrid thermoelectrics for energy harvesting. Koumoto can be reached by email at koumoto@apchem.nagoya-u.ac.jp . Jun Chen is currently a Full Professor at IPRI|ACES, Australian Institute for Innovative Materials, University of Wollongong (UOW). He received BE from Zhejiang University of Technology (1995) and PhD from UOW (2003). Professor Chen has published more than 180 papers and has been named a Highly Cited Researcher for 2018 (Clarivate Analytics). His research interests include sustainable energy devices, electro-/bio-interfaces, nano-/micro-materials, 2D/3D printing, and smart wearable electronic devices. Apart from academic achievements, Chen has also been involved in two spin-off companies: AQUAHYDREX as one of the key inventors, and IMAGINE as one of the scientific founders.

Analysis of Power Consumption Efficiency on Various IoT and Cloud Based Wireless Health Monitoring Systems: A Survey

Analysis of Power Consumption Efficiency on Various IoT and Cloud Based Wireless Health Monitoring Systems: A Survey

Development of a Wireless Health Monitoring System for Measuring Core Body Temperature from the Back of the Body.

In this paper, a user-friendly and low-cost wireless health monitoring system that measures skin temperature from the back of the body for monitoring the core body temperature is proposed. To measure skin temperature accurately, a semiconductor-based microtemperature sensor with a maximum accuracy of ±0.3°C was chosen and controlled by a high-performance/low-power consumption Acorn-Reduced Instruction Set Computing Machine (ARM) architecture microcontroller to build the temperature measuring device. Relying on a 2.4 GHz multichannel Gaussian frequency shift keying (GFSK) RF communication technology, up to 100 proposed temperature measuring devices can transmit the data to one receiver at the same time. The shell of the proposed wireless temperature-measuring device was manufactured via a 3D printer, and the device was assembled to conduct the performance tests and in vivo experiments. The performance test was conducted with a K-type temperature sensor in a temperature chamber to observe temperature measurement performance. The results showed an error value between two devices was less than 0.1°C from 25 to 40°C. For the in vivo experiments, the device was attached on the back of 10 younger male subjects to measure skin temperature to investigate the relationship with ear temperature. According to the experimental results, an algorithm based on the curve-fitting method was implemented in the proposed device to estimate the core body temperature by the measured skin temperature value. The algorithm was established as a linear model and set as a quadratic formula with an interpolant and with each coefficient for the equation set with 95% confidence bounds. For evaluating the goodness of fit, the sum of squares due to error (SSE), R-square, adjusted R-square, and root mean square error (RMSE) values were 33.0874, 0.0212, 0.0117, and 0.3998, respectively. As the experimental results have shown, the mean value for an error between ear temperature and estimated core body temperature is about ±0.19°C, and the mean bias is 0.05 ± 0.14°C when the subjects are in steady status.

Opportunities and Challenges in Health Sensing for Extreme Industrial Environment: Perspectives From Underground Mines

Occupational health and safety hazards in the extreme work environment of underground mines remained a serious concern for both mine management and regulatory agency. Miners are often employed to perform different mining activities across the mine and are always exposed to health risks such as lung cancer. With the technology advancements, it is now possible to keep track of mine individuals and their health parameters. The current practice of health analysis is periodic in nature and is highly dependent on voluntary participation. Wearable health sensing system is an alternative solution to overcome these challenges and is able to provide insights on miners’ health conditions. Timely analysis of physiological parameters of the miners is immensely helpful to minimize the injuries and can also provide preventive measures for potential health hazards. In this paper, we propose a wireless health monitoring system, especially for underground mines. The contributions of this paper are twofold. First, it presents and discusses our proposed system architecture and solution followed by challenges of such system in the context of underground mines. Second, as a preliminary analysis, detailed discussion on the wireless link behavior for reliable data transmission and communication are presented. We performed real-world experimental measurements in an operational underground coal mine considering several deployment settings in straight, near face and curved mine galleries. The communication metrics (e.g., received signal strength and packet reception rate) are extensively evaluated.

Design and implementation of blue tooth aided inexpensive safety band for fishermen

This paper presents with a new reliable inexpensive wireless health monitoring system designed for fishermen. Presently there are many smart devices available for the general population with many features in them that not put to use in every situation of our life. The most important aim of this paper is to deal with the safety and well-being of the fishermen during their voyage. The major cause of fishermen’s poor health during voyage is due to lack of proper health guidance to the fishermen. This paper proposes with the monitoring of the fisherman’s health through various sensors and relaying of those signals through blue tooth connectivity. The monitoring signals are finally obtained in Android mobile devices that can be used by the crew in real-time. The proposed approach is feasible for fishermen by establishing a software application to monitor the health of the fisherman. The proposed system is simple, economical and specifically designed for the people of the fishing community and it also enhances the motor control of the boat through the software interface by using a set of soft keys.

A Wireless Health Monitoring System Using Mobile Phone Accessories

This paper presents the design and prototype of a wireless health monitoring system using mobile phone accessories. We focus on measuring real-time electrocardiogram (ECG) and heart rate monitoring using a smartphone case. With the increasing number of cardiac patients worldwide, this design can be used for early detection of heart diseases. Unlike most of the existing methods that use an optical sensor to monitor heart rate, our approach is to measure real-time ECG with dry electrodes placed on smartphone case. The collected ECG signal can be stored and analyzed in real time through a smartphone application for prognosis and diagnosis. The proposed hardware system consists of a single chip microcontroller (RFduino) embedded with Bluetooth low energy, hence miniaturizing the size and prolonging battery life. The system called “smart case” has been tested in a laboratory environment. We also designed a 3-D printed smartphone case to validate the feasibly of the system. The results demonstrated that the proposed system could be comparable to medical grade devices.

Wireless Health Monitoring System for Rotor Eccentricity Faults Detection in Induction Machine

Condition monitoring and fault detection of induction machines become an important area of research. Many techniques have been applied in this field including vibration, thermal, chemical and acoustic emission monitoring, but Motor Current Signature Analysis monitoring techniques usually are applied to detect the various classes of induction machine faults such as rotor, short winding, eccentricity and bearing fault etc. This paper presents a wireless system detection for rotor eccentricity faults in induction machine based on LabVIEW platform. Moreover, it is demonstrated that eccentricity fault generates a series of low frequency components in the form of sidebands around the fundamental frequency and its harmonic. In addition, the amplitudes of those components increase in proportion to the load and fault severity. The Power Spectral Density techniques and Short Time Frequency Transform spectrogram of current signals are used to detect the presence of those fault signatures.