Wearable Health Monitoring Systems Research Articles

Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision

HighlightsA novel optoelectronic synapse compatible with existing chip technology is demonstrated. This device excels in mimicking memory and learning functions using light, making it ideal for future neuromorphic computing in biomedicine.Our design surpasses previous models with its ability to switch between short-term and long-term memory states using light pulses.Experiments on real-world biomedical data (electroencephalogram, electromyography, electrocardiogram) showed significant improvement in classification accuracy. This highlights the device's potential for advanced physiological signal processing and wearable health monitoring systems.

Effect of Nanostructure Morphology and Concentration on the Piezoelectric Performance of Flexible Pressure Sensor based on PVDFTrFE/ Nano-ZnO Composite Thin Film

Background: The development of high-performance piezoelectric pressure sensors with outstanding sensitivity, good linearity, flexibility, durability, and biocompatibility is of great significance for smart robotics, human healthcare devices, smart sensors, and electronic skin. Thus, considerable progress has been achieved in enhancing the piezoelectric property of PVDF-TrFEbased composite pressure sensors by adding various ZnO nanostructures in PVDF-TrFE polymer acting as a nucleating agent and dielectric material. Aims: In this work, flexible pressure sensors with a sandwich structure based on PVDFTrFE/ nano-ZnO composite sensing film were fabricated using a simple spin-coating method and post-annealing process, while electrospinning and high-voltage polarization processes were not adopted. Methods: Poly (vinylidene fluoride-trifluoroethylene) (PVDF-TrFE)/nano-ZnO composite films were prepared via spin coating to fabricate flexible piezoelectric pressure sensors. ZnO nanoparticles (ZnO NPs), tetrapod ZnO (T-ZnO) and ZnO nanorods (ZnO NRs) were used as nano-fillers for piezoelectric PVDF-TrFE, to enhance the beta-crystal ratio as well as the crystallinity of PVDF-TrFE. The structural and surface morphologies of the composite films were investigated using Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD) and scanning electron microscopy (SEM). Results: Among three different types of ZnO nanostructures with a concentration range (0-7.5 wt%), the sensor with 0.75 wt% ZnO NRs nanofiller exhibits a maximum output voltage of 1.73 V under an external pressure of 3 N and a maximum sensitivity of 586.3 mV/N at the range of 0-3 N. Further, the sensor can generate a clear piezoelectric voltage under bending and twisting deformation as well as compression and tensile deformation. Conclusion: To summarize, the addition of different concentrations of nano-ZnO can remarkably improve the piezoelectric performance of the composite sensor, and ZnO NRs can achieve better piezoelectric properties of the sensor as compared to ZnO NPs and T-ZnO. In addition, the sensor with 0.75 wt% ZnO NRs as nanofiller has the highest piezoelectric response, which is about 2.4 times that of the pure PVDF-TrFE sensor. It is demonstrated that the sensor has great potential applications in wearable health monitoring systems and mechanical stress measurement electronics.

YELLOW LIGHT THE NEXT TECHNOLOGY FOR SMART WATCHES

Light emitting diodes (LED) are often found in wearable smart electronic devices, such as smart watches, health care monitoring system etc. These are utilized for photoplethysmography (PPG) to monitor a variety of physical health factors. Wearable health monitoring systems have been developed as an outcome of the growth of sensor-based process to deliver real-time feedback information to promote health. This technique has the potential to help with symptom checking and treatment in sophisticated diseases, such as pain management.

Retracted: Fog-Driven Secure Authentication and Key Exchange Scheme for Wearable Health Monitoring System

Retracted: Fog-Driven Secure Authentication and Key Exchange Scheme for Wearable Health Monitoring System

A new particle filter algorithm filtering motion artifact noise for clean electrocardiogram signals in wearable health monitoring system.

With the evolution of wearable systems, more and more people tend to wear wearable devices for health monitoring during sports. However, a large amount of motion artifact noise is introduced at this time, which is difficult to filter out due to its stochasticity. The amplitude and characteristics of motion artifact noise vary with changes in motion intensity. In order to filter out the motion artifact noise, the paperproposes a new particle algorithm, which can detect the intensity of the motion artifact for adaptive filtering, especiallysuitable for wearable health monitoring systems. In this algorithm, variational mode decomposition was first introduced to analyze the noisy electrocardiogram (ECG) signal in order to find the clean components. Then, the Laguerre estimation technique was applied to obtain an accurate ECG polar model. Taking this model as the state equation, a particle filter algorithm was defined to filter out the motion artifact noise. In the particle filter algorithm, we defined a parameter γ whose values were obtained from the six-axis data of motion sensor MPU6050 in our wearable device. This parameter γ could reflect the current noise levels and adaptively update the particle weights. Finally, some exercise experiments proved that the parameter γ could map the motion artifacts in real time and also demonstrated the superiority of the algorithm in terms of signal-to-noise ratio improvement and error reduction compared to other algorithms. The new particle filter algorithm proposed in this paper combines the six-axis data (three-axis accelerometer and three-axis gyroscope) with the ECG signal to effectively eliminate a large amount of motion artifact noise, thus solving the problem of excess noise from wearable devices when people are exercising, allowing them to accurately obtain real-time ECG health information.

Integrating Electronics and Biomedical Applications in 3D Printing Current Progress and Future Possibilities

Biomedical three-dimensional(3D) printing has redefined the landscape of medical practice, facilitating the customization of implants, prosthetics, and medical instruments. The use of 3D printing, also known as additive manufacturing, empowers healthcare professionals to create patient-specific medical solutions that align seamlessly with anatomical complexities. Through the deposition of material layer by layer, 3D printing allows for the fabrication of intricately designed structures with precision and reproducibility. The medical field has witnessed a paradigm shift as personalized implants and prosthetics are tailored to the unique needs of patients, thereby improving functionality and patient outcomes. This review aims to provide a comprehensive analysis of the integration of electronics and biomedical 3D printing. The focus extends beyond individual domains, encapsulating the crossroads where the two converge to form synergistic solutions. Each subsequent section of this review will delve into specific facets, discussing current advances, challenges, and potential future trajectories. From 3D printed medical devices with embedded electronics to wearable health monitoring systems, this review seeks to unearth the emerging landscape of interdisciplinary innovation.

Bioinspired Ultra‐Robust Ionogels Constructed with Soft‐Rigid Confinement Space for Multimodal Monitoring Electronics

AbstractIonogels are compelling materials for flexible hybrid electronics owing to their attractive physical properties and infinite adjustability of chemical structures. However, ionogels must be sufficiently strong to ensure durability, stability, and a wide range of strains in various applications to make electronic systems mechanically compliant. Inspired by the hierarchical structure of multiphase substances in the skin, it is fabricated several transparent (>90%) and ultra‐robust (tensile strength >17 MPa, toughness >40 MJ m−3, and elongation ≈300%) ionogels via in situ polymerization of polymers with the different binding abilities to the ionic liquid by forming soft and rigid confinement space. This strategy can also be applied to other ionic liquids and polymers. Furthermore, the designed ionogel sensors can be used to develop a wearable intelligent health monitoring system capable of monitoring health‐related physiological signals, such as temperature, body tremors, wrist pulse, breathing, and gestures, for predicting and responding to emergencies, which will pave the way for wearable security monitoring technology.

Ultrathin Self-Powered Heavy-Metal-Free Cu-In-Se Quantum Dot Photodetectors for Wearable Health Monitoring.

Mechanically deformable photodetectors (PDs) are key device components for wearable health monitoring systems based on photoplethysmography (PPG). Achieving high detectivity, fast response time, and an ultrathin form factor in the PD is highly needed for next-generation wearable PPG systems. Self-powered operation without a bulky power-supply unit is also beneficial for point-of-care application. Here, we propose ultrathin self-powered PDs using heavy-metal-free Cu-In-Se quantum dots (QDs), which enable high-performance wearable PPG systems. Although the light-absorbing QD layer is extremely thin (∼40 nm), the developed PD exhibits excellent performance (specific detectivity: 2.10 × 1012 Jones, linear dynamic range: 102 dB, and spectral range: 250-1050 nm at zero bias), which is comparable to that of conventional rigid QD-PDs employing thick Pb-chalcogenide QD layers. This is attributed to material and device strategies─materials that include Cu-In-Se QDs, a MoS2-nanosheet-blended poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) hole transport layer, a ZnO nanoparticle electron transport layer, Ag and ITO electrodes, and an ultrathin form factor (∼120 nm except the electrodes) that enable excellent mechanical deformability. These allow the successful application of QD-PDs to a wearable system for real-time PPG monitoring, expanding their potential in the field of mobile bioelectronics.

Highly Sensitive Soft Pressure Sensors for Wearable Applications Based on Composite Films with Curved 3D Carbon Nanotube Structures.

Soft pressure sensors based on 3D microstructures exhibit high sensitivity in the low-pressure range, which is crucial for various wearable and soft touch applications. However, it is still a challenge to manufacture soft pressure sensors with sufficient sensitivity under small mechanical stimuli for wearable applications. This work presents a novel strategy for extremely sensitive pressure sensors based on the composite film with local changes in curved 3D carbon nanotube (CNT) structure via expandable microspheres. The sensitivity is significantly enhanced by the synergetic effects of heterogeneous contact of the microdome structure and changes of percolation network within the curved 3D CNT structure. The finite-element method simulation is used to comprehend the relationships between the sensitivity and mechanical/electrical behavior of microdome structure under the applied pressure. The sensor shows an excellent sensitivity (571.64 kPa-1 ) with fast response time (85ms), great repeatability, and long-term stability. Using the developed sensor, a wireless wearable health monitoring system to avoid carpel tunnel syndrome is built, and a multi-array pressure sensor for realizing a variety of movements in real-time is demonstrated.

Cloud-Assisted Secure and Cost-Effective Authenticated Solution for Remote Wearable Health Monitoring System

Security and privacy are the leading solicitude in cloud computing since users have restricted privilege on the data maintained by distinct service providers at remote locations. The situation becomes more strenuous when a huge amount of data is sensed and produced by wearable devices as it is usually considered more sensitive and heterogeneous. The majority of existing authenticated solutions for such a challenging environment either suffer due to high computation and communication costs or due to the fact that they are susceptible to known cyber threats making them impractical for utilization. Therefore we introduced a cost-effective and secure authenticated scheme for the cloud-assisted remote wearable health monitoring system. Informal security proof is discussed to showcase the security strength of the proposed scheme against known security threats. After that a detailed performance analysis of the proposed scheme with related protocols reveals the fact that it can reduce the computation and communication costs by more than 37% and 40%, respectively. Moreover, our scheme offers some added security features like physical and desynchronization attacks prevention.

Retraction Note: A fuzzy model of wearable network real-time health monitoring system on pharmaceutical industry

Retraction Note: A fuzzy model of wearable network real-time health monitoring system on pharmaceutical industry

Graphene-Based Wearable Temperature Sensors: A Review.

Flexible sensing electronics have received extensive attention for their potential applications in wearable human health monitoring and care systems. Given that the normal physiological activities of the human body are primarily based on a relatively constant body temperature, real-time monitoring of body surface temperature using temperature sensors is one of the most intuitive and effective methods to understand physical conditions. With its outstanding electrical, mechanical, and thermal properties, graphene emerges as a promising candidate for the development of flexible and wearable temperature sensors. In this review, the recent progress of graphene-based wearable temperature sensors is summarized, including material preparation, working principle, performance index, classification, and related applications. Finally, the challenges and future research emphasis in this field are put forward. This review provides important guidance for designing novel and intelligent wearable temperature-sensing systems.

IoT-based wearable health monitoring device and its validation for potential critical and emergency applications.

The COVID-19 pandemic brought the world to a standstill, posing unprecedented challenges for healthcare systems worldwide. The overwhelming number of patients infected with the virus placed an enormous burden on healthcare providers, who struggled to cope with the sheer volume of cases. Furthermore, the lack of effective treatments or vaccines means that quarantining has become a necessary measure to slow the spread of the virus. However, quarantining places a significant burden on healthcare providers, who often lack the resources to monitor patients with mild symptoms or asymptomatic patients. In this study, we propose an Internet of Things (IoT)-based wearable health monitoring system that can remotely monitor the exact locations and physiological parameters of quarantined individuals in real time. The system utilizes a combination of highly miniaturized optoelectronic and electronic technologies, an anti-epidemic watch, a mini-computer, and a monitor terminal to provide real-time updates on physiological parameters. Body temperature, peripheral oxygen saturation (SpO2), and heart rate are recorded as the most important measurements for critical care. If these three physiological parameters are aberrant, then it could represent a life-endangering situation and/or a short period over which irreversible damage may occur. Therefore, these parameters are automatically uploaded to a cloud database for remote monitoring by healthcare providers. The monitor terminal can display real-time health data for multiple patients and provide early warning functions for medical staff. The system significantly reduces the burden on healthcare providers, as it eliminates the need for manual monitoring of patients in quarantine. Moreover, it can help healthcare providers manage the COVID-19 pandemic more effectively by identifying patients who require medical attention in real time. We have validated the system and demonstrated that it is well suited to practical application, making it a promising solution for managing future pandemics. In summary, our IoT-based wearable health monitoring system has the potential to revolutionize healthcare by providing a cost-effective, remote monitoring solution for patients in quarantine. By allowing healthcare providers to monitor patients remotely in real time, the burden on medical resources is reduced, and more efficient use of limited resources is achieved. Furthermore, the system can be easily scaled to manage future pandemics, making it an ideal solution for managing the health challenges of the future.

Wearable Optical Fiber Sensors for Biomechanical Measurement in Medical Rehabilitation: A Review

With the increasing demand for rehabilitation of the elderly population and patients with limb injuries, it is imperative to develop new wearable health monitoring system and assistive rehabilitation equipment. Optical fiber sensing has the advantages of light-weight, anti-electromagnetic interference, higher strain and elastic limit, safety and dependableness. It has been significantly utilized in engineering prospecting, building structure monitoring, and medical and health fields. At present, the application of optical fiber sensing in medical rehabilitation has been developed rapidly, however, the discussion and summary of its key theories and technologies are insufficient. This review details different types of optical fiber sensing mechanisms and devices, as well as the measurement of physical parameters using optical fiber sensors and application examples in the field of medical rehabilitation. This review systematically analyzes the advance of the sensitivity, flexibility, accuracy, and wearability of the optical fiber sensing in the monitoring of human body sign information. This review also discusses the challenges of making a new generation of optical fiber sensors that fit human joints, muscles, and rehabilitation equipment. Finally, this review provides a research direction for the realization of flexible, comfortable, safe, stable monitoring of human biomechanical parameters and real-time precise control of rehabilitation equipment in the future.

Multifunctional Wearable System for Mapping Body Temperature and Analyzing Sweat.

Integrated wearable bioelectronic health monitoring systems have revealed new possibilities for collecting personalized physiological information. Wearable sweat sensors have the potential to noninvasively measure valuable biomarkers. Mapping sweat and skin-temperature throughout the body can provide detailed information on the human body. However, existing wearable systems cannot evaluate such data. Here, we report a multifunctional wearable platform that can wirelessly measure local sweat loss, sweat chloride concentration, and skin temperature. The approach combines a reusable electronics module to monitor skin temperature and a microfluidic module for monitoring sweat loss and sweat chloride concentration. The miniaturized electronic system takes temperature measurements from the skin and wirelessly transmits the obtained data to a user device using Bluetooth technology. In contrast, the microfluidic system provides accurate colorimetric analysis of the chloride concentration and sweat loss. Thus, this integrated wearable system has great application potential in individualized health management systems for sports researchers and competitors and can also be applied in clinical settings.

Development of a wireless wearable Health Monitoring System with weband mobile tracking system

Health Monitoring Systems (HMS) is becoming increasingly ubiquitous supported by recent technological advances and influenced by high healthcare costs. Health is a necessary aspect in our daily routine and it would make it easier for people to act upon the condition that they are having with these systems. This research paper aims to develop and design a scalable and feasible model of an HMS through advanced sensor and communication technology. The Proof of Concept can be achieved by utilizing low-cost sensors and microprocessors such as heart rate sensors, MAX30205 body temperature sensor, and the NodeMCU. The NodeMCU is an amazing little chip that has onboard WiFi capabilities and will be the way in communicating to Adafruit IO through MQTT protocol. Adafruit IO has feeds and dashboards that will serve as a visual representation of the metadata about the sensor data you push to Adafruit IO from the NodeMCU board. This technology can increase the capacity of physicians to treat and monitor more patients, can offer better access to healthcare, and assures patients to get that daily information about their conditions.

Review on Wearable Sensors of the Medical System

A framework known as "smart healthcare" makes use of technologies like wearable's, and the Internet of Medical Things (IoT), In order to intelligently manage and respond to the needs of the health environment, we need to combine data, machine learning algorithms, wireless communication technology, and the Internet of Things (IoT) to link people, resources, and organizations; make health records easily accessible; and share the data widely. Medical sensors, or IoT, are one of the key components of smart healthcare. Due to the complexity of illnesses, Disease diagnosis often necessitates the use of multiple types of medical signals. The most crucial concern when employing multimodal signals is how to fuse them, which is a topic of growing interest among researchers. The newest generation of personal portable gadgets for telemedicine practice is wearable health monitoring systems. However, due to the inapplicability of the building process used for standard semiconductor equipment, wearable healthcare equipment research and commercialization are currently progressing at a very sluggish rate. Despite these obstacles, developments in materials flag, chemical analysis techniques, apparatus, and manufacturing processes created a groundwork for whole new wearable technology, which has continued to evolve. &nbsp

A Machine-Learning-Algorithm-Assisted Intelligent System for Real-Time Wireless Respiratory Monitoring

Respiratory signals are basic indicators of human life and health that are used as effective biomarkers to detect respiratory diseases in clinics, including cardiopulmonary function, breathing disorders, and breathing system infections. Therefore, it is necessary to continuously measure respiratory signals. However, there is still a lack of effective portable electronic devices designed to meet the needs of daily respiratory monitoring. This study presents an intelligent, portable, and wireless respiratory monitoring system for real-time evaluation of human respiratory behaviors. The system consists of a triboelectric respiratory sensor; circuit board hardware for data acquisition, preprocessing, and wireless transmission; a machine learning algorithm for enhancing recognition accuracy; and a mobile terminal app. The triboelectric sensor—fabricated by the screen-printing method—is lightweight, non-invasive, and biocompatible. It provides a clear response to the frequency and intensity of respiratory airflow. The portable circuit board is reusable and cost-effective. The decision tree model algorithm is used to identify the respiratory signals with an average accuracy of 97.2%. The real-time signal and statistical results can be uploaded to a server network and displayed on various mobile terminals for body health warnings and advice. This work promotes the development of wearable health monitoring systems.

Spider-inspired tunable mechanosensor for biomedical applications

The recent advances of wearable sensors are remarkable but there are still limitations that they need to be refabricated to tune the sensor for target signal. However, biological sensory systems have the inherent potential to adjust their sensitivity according to the external environment, allowing for a broad and enhanced detection. Here, we developed a Tunable, Ultrasensitive, Nature-inspired, Epidermal Sensor (TUNES) that the strain sensitivity was dramatically increased (GF ~30k) and the pressure sensitivity could be tuned (10–254 kPa−1) by preset membrane tension. The sensor adjusts the sensitivity to the pressure regime by preset tension, so it can measure a wide range (0.05 Pa–25 kPa) with the best performance: from very small signals such as minute pulse to relatively large signals such as muscle contraction and respiration. We verified its capabilities as a wearable health monitoring system by clinical trial comparing with pressure wire which is considered the current gold standard of blood pressure (r = 0.96) and home health care system by binary classification of Old’s/Young’s pulse waves via machine learning (accuracy 95%).

Non-contact wearable synchronous measurement method of electrocardiogram and seismocardiogram signals.

Cardiovascular disease is one of the leading threats to human lives and its fatality rate still rises gradually year by year. Driven by the development of advanced information technologies, such as big data, cloud computing, and artificial intelligence, remote/distributed cardiac healthcare is presenting a promising future. The traditional dynamic cardiac health monitoring method based on electrocardiogram (ECG) signals only has obvious deficiencies in comfortableness, informativeness, and accuracy under motion state. Therefore, a non-contact, compact, wearable, synchronous ECG and seismocardiogram (SCG) measuring system, based on a pair of capacitance coupling electrodes with ultra-high input impedance, and a high-resolution accelerometer were developed in this work, which can collect the ECG and SCG signals at the same point simultaneously through the multi-layer cloth. Meanwhile, the driven right leg electrode for ECG measurement is replaced by the AgCl fabric sewn to the outside of the cloth for realizing the total gel-free ECG measurement. Besides, synchronous ECG and SCG signals at multiple points on the chest surface were measured, and the recommended measuring points were given by their amplitude characteristics and the timing sequence correspondence analysis. Finally, the empirical mode decomposition algorithm was used to adaptively filter the motion artifacts within the ECG and SCG signals for measuring performance enhancement under motion states. The results demonstrate that the proposed non-contact, wearable cardiac health monitoring system can effectively collect ECG and SCG synchronously under various measuring situations.