Wearable Pendulum-Rotor-Separated Hybrid Generator for Smart Healthcare Monitoring.

Human biomechanical energy, with features of fluctuating amplitudes and low frequency, has been considered as a potential sustainable power source for wearable healthcare monitoring devices. Developing an effective energy harvester to ensure robust energy harvesting efficiency remains highly desired. Herein, we propose a wearable pendulum-rotor-separated triboelectric-electromagnetic hybrid generator (PTEHG). The novel pendulum-rotor separation design can make the rotor propelled in one direction by the swinging pendulum, which can further facilitate a wearable hybrid energy harvester with stable energy harvesting, a broad operating bandwidth, and system reliability. By converting the biomechanical energy into electric power, the peak power density of 83.12 W/m3 is delivered by the PTEHG at a frequency of 1.6 Hz. A PTEHG-based healthcare monitoring system was also demonstrated for real-time motion tracking and fall detection. This work paves a new way for enhancing the efficiency of human biomechanical energy harvesting and presents a practical pathway for continuous healthcare monitoring.

Advancements in Flexible Electronics Fabrication: Film Formation, Patterning, and Interface Optimization for Cutting-Edge Healthcare Monitoring Devices.

Flexible electronics can seamlessly adhere to human skin or internal tissues, enabling the collection of physiological data and real-time vital sign monitoring in home settings, which give it the potential to revolutionize chronic disease management and mitigate mortality rates associated with sudden illnesses, thereby transforming current medical practices. However, the development of flexible electronic devices still faces several challenges, including issues pertaining to material selection, limited functionality, and performance instability. Among these challenges, the choice of appropriate materials, as well as their methods for film formation and patterning, lays the groundwork for versatile device development. Establishing stable interfaces, both internally within the device and in human-machine interactions, is essential for ensuring efficient, accurate, and long-term monitoring in health electronics. This review aims to provide an overview of critical fabrication steps and interface optimization strategies in the realm of flexible health electronics. Specifically, we discuss common thin film processing methods, patterning techniques for functional layers, interface challenges, and potential adjustment strategies. The objective is to synthesize recent advancements and serve as a reference for the development of innovative flexible health monitoring devices.

Integrating meta-heuristic with named data networking for secure edge computing in IoT enabled healthcare monitoring system.

The advancement in technology, with the "Internet of Things (IoT) is continuing a crucial task to accomplish distance medical care observation, where the effective and secure healthcare information retrieval is complex. However, the IoT systems have restricted resources hence it is complex to attain effective and secure healthcare information acquisition. The idea of smart healthcare has developed in diverse regions, where small-scale implementations of medical facilities are evaluated. In the IoT-aided medical devices, the security of the IoT systems and related information is highly essential on the other hand, the edge computing is a significant framework that rectifies their processing and computational issues. The edge computing is inexpensive, and it is a powerful framework to offer low latency information assistance by enhancing the computation and the transmission speed of the IoT systems in the medical sectors. The main intention of this work is to design a secure framework for Edge computing in IoT-enabled healthcare systems using heuristic-based authentication and "Named Data Networking (NDN)". There are three layers in the proposed model. In the first layer, many IoT devices are connected together, and using the cluster head formation, the patients are transmitting their data to the edge cloud layer. The edge cloud layer is responsible for storage and computing resources for rapidly caching and providing medical data. Hence, the patient layer is a new heuristic-based sanitization algorithm called Revised Position of Cat Swarm Optimization (RPCSO) with NDN for hiding the sensitive data that should not be leaked to unauthorized users. This authentication procedure is adopted as a multi-objective function key generation procedure considering constraints like hiding failure rate, information preservation rate, and degree of modification. Further, the data from the edge cloud layer is transferred to the user layer, where the optimal key generation with NDN-based restoration is adopted, thus achieving efficient and secure medical data retrieval. The framework is evaluated quantitatively on diverse healthcare datasets from University of California (UCI) and Kaggle repository and experimental analysis shows the superior performance of the proposed model in terms of latency and cost when compared to existing solutions. The proposed model performs the comparative analysis of the existing algorithms such as Cat Swarm Optimization (CSO), Osprey Optimization Algorithm (OOA), Mexican Axolotl Optimization (MAO), Single candidate optimizer (SCO). Similarly, the cryptography tasks like "Rivest-Shamir-Adleman (RSA), Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), and Data sanitization and Restoration (DSR) are applied and compared with the RPCSO in the proposed work. The results of the proposed model is compared on the basis of the best, worst, mean, median and standard deviation. The proposed RPCSO outperforms all other models with values of 0.018069361, 0.50564046, 0.112643119, 0.018069361, 0.156968355 and 0.283597992, 0.467442652, 0.32920734, 0.328581887, 0.063687386 for both dataset 1 and dataset 2 respectively.

Ultralow Power, Cleft Size-Adjustable and pH-Sensitive Hyaluronic Acid (HA) Biodevices for Acid-Sensing Ion Channels Emulation.

The burgeoning implantable biodevices have unlocked new frontiers in healthcare, promising personalized monitoring strategies tailored to specific needs. Herein, hyaluronic acid (HA) is harnessed to create fully biocompatible, acidity-sensitivity and cleft-adjustable neuromorphic devices. These HA-biodevices exhibit remarkable sensitivity to pH variations, effectively mimicking biological acid-sensing ion channels (ASICs) through protonation reactions between electronegative atoms and hydrogen ions, even at ultralow driving voltage (5 mV). They can monitor joint cartilage acidity by tracking changes in proton concentration and successfully diagnose the onset of arthritis. Furthermore, by adjusting the synaptic device's cleft distance, which determines responsiveness, power efficiency and plasticity, HA-based neuromorphic devices can be tailored to meet the unique demands of various implantation sites, providing both high-sensitivity and low-heat dissipation, thus broadening their application scopes. Moreover, the HA-biodevices maintain stable performance across various bending degrees, up to a curvature radius of 7.5 mm, with flexibility and deformation resilience enabling installation on joints of varying curvatures. The combination of all-biocompatibility, high sensitivity, low heat dissipation, ultralow low power (2 pW), and extraordinary deformation tolerance paves the way for the development of versatile, multipurpose medical monitoring devices with immense potential in the field of healthcare.

Valorization of Food Waste: Utilizing Natural Porous Materials Derived from Pomelo-Peel Biomass to Develop Triboelectric Nanogenerators for Energy Harvesting and Self-Powered Sensing.

Food waste is an enormous challenge, with implications for the environment, society, and economy. Every year around the world, 1.3 billion tons of food are wasted or lost, and food waste-associated costs are around $2.6 trillion. Waste upcycling has been shown to mitigate these negative impacts. This study's optimized pomelo-peel biomass-derived porous material-based triboelectric nanogenerator (PP-TENG) had an open circuit voltage of 58 V and a peak power density of 254.8 mW/m2. As porous structures enable such triboelectric devices to respond sensitively to external mechanical stimuli, we tested our optimized PP-TENG's ability to serve as a self-powered sensor of biomechanical motions. As well as successfully harvesting sufficient mechanical energy to power light-emitting diodes and portable electronics, our PP-TENGs successfully monitored joint motions, neck movements, and gait patterns, suggesting their strong potential for use in healthcare monitoring and physical rehabilitation, among other applications. As such, the present work opens up various new possibilities for transforming a prolific type of food waste into value-added products and thus could enhance long-term sustainability while reducing such waste.

Ultrahigh Sensitivity for Thermographic Human-Machine Interface via Precious Metals Atomic Layer Deposition on V-MXene: Computational and Experimental Exploration.

Global healthcare based on the Internet of Things system is rapidly transforming to measure precise physiological body parameters without visiting hospitals at remote patients and associated symptoms monitoring. 2D materials and the prevailing mood of current ever-expanding MXene-based sensing devices motivate to introduce first the novel iridium (Ir) precious metal incorporated vanadium (V)-MXene via industrially favored emerging atomic layer deposition (ALD) techniques. The current work contributes a precise control and delicate balance of Ir single atomic forms or clusters on the V-MXene to constitute a unique precious metal-MXene embedded heterostructure (Ir-ALD@V-MXene) in practical real-time sensing healthcare applications to thermography with human-machine interface for the first time. Ir-ALD@V-MXene delivers an ultrahigh durability and sensing performance of 2.4% °C-1 than pristine V-MXene (0.42% °C-1), outperforming several conventionally used MXenes, graphene, underscoring the importance of the Ir-ALD innovative process. Aberration-corrected advanced ultra-high-resolution transmission/scanning transmission electron microscopy confirms the presence of Ir atomic clusters on well-aligned 2D-layered V-MXene structure and their advanced heterostructure formation (Ir-ALD@V-MXene), enhanced sensing mechanism is investigated using density functional theory (DFT) computations. A rational design empowering the Ir-ALD process on least explored V-MXene can potentially unfold further precious metals ALD-process developments for next-generation wearable personal healthcare devices.

Flexible Nanofiber Pressure Sensors with Hydrophobic Properties for Wearable Electronics.

In recent years, flexible pressure sensors have received considerable attention for their potential applications in health monitoring and human-machine interfaces. However, the development of flexible pressure sensors with excellent sensitivity performance and a variety of advantageous characteristics remains a significant challenge. In this paper, a high-performance flexible piezoresistive pressure sensor, BC/ZnO, is developed with a sensitive element consisting of bacterial cellulose (BC) nanofibrous aerogel modified by ZnO nanorods. The BC/ZnO pressure sensor exhibits excellent mechanical and hydrophobic properties, as well as a high sensitivity of -15.93 kPa-1 and a wide range of detection pressure (0.3-20 kPa), fast response (300 ms), and good cyclic durability (>1000). Furthermore, the sensor exhibits excellent sensing performance in real-time monitoring of a wide range of human behaviors, including mass movements and subtle physiological signals.

Recent Advances in Two-Dimensional Nanomaterials for Healthcare Monitoring.

The development of advanced technologies for the fabrication of functional nanomaterials, nanostructures, and devices has facilitated the development of biosensors for analyses. Two-dimensional (2D) nanomaterials, with unique hierarchical structures, a high surface area, and the ability to be functionalized for target detection at the surface, exhibit high potential for biosensing applications. The electronic properties, mechanical flexibility, and optical, electrochemical, and physical properties of 2D nanomaterials can be easily modulated, enabling the construction of biosensing platforms for the detection of various analytes with targeted recognition, sensitivity, and selectivity. This review provides an overview of the recent advances in 2D nanomaterials and nanostructures used for biosensor and wearable-sensor development for healthcare and health-monitoring applications. Finally, the advantages of 2D-nanomaterial-based devices and several challenges in their optimal operation have been discussed to facilitate the development of smart high-performance biosensors in the future.

Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review.

BACKGROUND: The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies. OBJECTIVE: The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home. METHODS: A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis. RESULTS: Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used. CONCLUSION: Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

An autonomous wheelchair with health monitoring system based on Internet of Thing.

Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users' four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.

Smart Contact Lenses for Healthcare Monitoring and Therapy.

The eye contains a wealth of physiological information and offers a suitable environment for noninvasive monitoring of diseases via smart contact lens sensors. Although extensive research efforts recently have been undertaken to develop smart contact lens sensors, they are still in an early stage of being utilized as an intelligent wearable sensing platform for monitoring various biophysical/chemical conditions. In this review, we provide a general introduction to smart contact lenses that have been developed for disease monitoring and therapy. First, different disease biomarkers available from the ocular environment are summarized, including both physical and chemical biomarkers, followed by the commonly used materials, manufacturing processes, and characteristics of contact lenses. Smart contact lenses for eye-drug delivery with advancing technologies to achieve more efficient treatments are then introduced as well as the latest developments for disease diagnosis. Finally, sensor communication technologies and smart contact lenses for antimicrobial and other emerging bioapplications are also discussed as well as the challenges and prospects of the future development of smart contact lenses.

Radiation Detector Front-End Readout Chip with Nonbinary Successive Approximation Register Analog-to-Digital Converter for Wearable Healthcare Monitoring Applications.

A 16-channel front-end readout chip for a radiation detector is designed for portable or wearable healthcare monitoring applications. The proposed chip reads the signal of the radiation detector and converts it into digital serial-out data by using a nonbinary successive approximation register (SAR) analog-to-digital converter (ADC) that has a 1-MS/s sampling rate and 10-b resolution. The minimum-to-maximum differential and integral nonlinearity are measured as -0.32 to 0.33 and -0.43 to 0.37 least significant bits, respectively. The signal-to-noise-and-distortion ratio and effective number of bits are 57.41 dB and 9.24 bits, respectively, for an input frequency of 500 kHz and a sampling rate of 1 MS/s. The SAR ADC has a 38.9-fJ/conversion step figure of merit at the sampling rate of 1 MS/s. The proposed chip can read input signals with peak currents ranging from 20 to 750 µA and convert the analog signal into a 10-bit serial-output digital signal. The input dynamic range is 2-75 pC. The resolution of the peak current is 208.3 nA. The chip, which has an area of 1.444 mm × 10.568 mm, is implemented using CMOS 0.18-µm 1P6M technology, and the power consumption of each channel is 19 mW. This design is suitable for wearable devices, especially biomedical devices.

An energy-aware heart disease prediction system using ESMO and optimal deep learning model for healthcare monitoring in IoT.

The Internet of Things (IoT), which provides seamless connectivity between people and things, improves our quality of life. In the medical field, predictive analytics can help transform a reactive healthcare (HC) strategy into a proactive one. The HC industry embraces cutting-edge artificial intelligence and machine learning (ML) technologies. ML's area of deep learning has the revolutionary potential to reliably analyze massive volumes of data quickly, produce insightful revelations and solve challenging issues. This article proposes an energy-aware heart disease prediction (HDP) system based on enhanced spider monkey optimization (ESMO) and a weight-optimized neural network for an IoT-based HC environment. The proposed work consists of two essential phases: energy-efficient data transmission and HDP. In energy-efficient transmission, the cluster leaders are optimally selected using ESMO and the cluster formation is done based on Euclidean distance. In HDP, the patient data are collected from the dataset, and essential features are extracted. After that, the dimensionality reduction is carried out using the modified linear discriminant analysis approach to reduce over-fitting issues. Finally, the HDP uses the enhanced Archimedes weight-optimized deep neural network (EAWO-DNN). The simulation findings demonstrate that the proposed optimal clustering mechanism enhances the network's lifespan by consuming minimal energy compared to the existing techniques. Also, the proposed EAWO-DNN classifier achieves higher prediction accuracy, precision, recall and f-measure than the conventional methods for predicting heart disease in IoT.Communicated by Ramaswamy H. Sarma.

Doping Engineering of Conductive Polymers and Their Application in Physical Sensors for Healthcare Monitoring.

Physical sensors have emerged as a promising technology for real-time healthcare monitoring, which tracks various physical signals from the human body. Accurate acquisition of these physical signals from biological tissue requires excellent electrical conductivity and long-term durability of the sensors under complex mechanical deformation. Conductive polymers, combining the advantages of conventional polymers and organic conductors, are considered ideal conductive materials for healthcare physical sensors due to their intrinsic conductive network, tunable mechanical properties, and easy processing. Doping engineering has been proposed as an effective approach to enhance the sensitivity, lower the detection limit, and widen the operational range of sensors based on conductive polymers. This approach enables the introduction of dopants into conductive polymers to adjust and control the microstructure and energy levels of conductive polymers, thereby optimizing their mechanical and conductivity properties. This review article provides a comprehensive overview of doping engineering methods to improve the physical properties of conductive polymers and highlights their applications in the field of healthcare physical sensors, including temperature sensors, strain sensors, stress sensors, and electrophysiological sensing. Additionally, the challenges and opportunities associated with conductive polymer-based physical sensors in healthcare monitoring are discussed.

[Application of electronic skin in healthcare and virtual reality].

Electronic skin has shown great application potential in many fields such as healthcare monitoring and human-machine interaction due to their excellent sensing performance, mechanical properties and biocompatibility. This paper starts from the materials selection and structures design of electronic skin, and summarizes their different applications in the field of healthcare equipment, especially current development status of wearable sensors with different functions, as well as the application of electronic skin in virtual reality. The challenges of electronic skin in the field of wearable devices and healthcare, as well as our corresponding strategies, are discussed to provide a reference for further advancing the research of electronic skin.

Hybrid deep learning model based smart IOT based monitoring system for Covid-19.

Recently, COVID-19 becomes a hot topic and explicitly made people follow social distancing and quarantine practices all over the world. Meanwhile, it is arduous to visit medical professionals intermittently by the patients for fear of spreading the disease. This IoT-based healthcare monitoring system is utilized by many professionals, can be accessed remotely, and provides treatment accordingly. In context with this, we designed an IoT-based healthcare monitoring system that sophisticatedly measures and monitors the parameters of patients such as oxygen level, blood pressure, temperature, and heart rate. This system can be widely used in rural areas that are linked to the nearest city hospitals to monitor the patients. The collected data from the monitoring system are stored in the cloud-based data storage and for the classification our approach proposes an innovative Recurrent Convolutional Neural Network (RCNN) based Puzzle optimization algorithm (PO). Based on the outcome further treatments are made with the assistance of physicians. Experimental analyses are made and analyzed the performance with state-of-art works. The availability of more data storage capacity in the cloud can make physicians access the previous data effortlessly.

Multilayer Perceptron Algorithm-Assisted Flexible Piezoresistive PDMS/Chitosan/cMWCNT Sponge Pressure Sensor for Sedentary Healthcare Monitoring.

Recently, the health problems faced by sedentary workers have received increasing attention. In this study, a pressure sensor based on a poly(dimethylsiloxane) (PDMS)/carboxylated chitosan (CCS)/carboxylated multiwalled carbon nanotube (cMWCNT) sponge was prepared to realize a portable, sensitive, comfortable, and noninvasive healthcare monitoring system for sedentary workers. The proposed piezoresistive pressure sensor exhibited exceptional sensing performances with high sensitivity (147.74 kPa-1), an ultrawide detection range (22 Pa to 1.42 MPa), and reliable stability (over 3000 cycles). Furthermore, the obtained sensor displayed superior capability in detecting various human motion signals. Based on the 4 × 4 sensing array and multilayer perceptron (MLP) algorithm model, a smart cushion was developed to recognize five types of sitting postures and supply timely reminders to sedentary workers. The piezoresistive sponge pressure sensor proposed in this study reveals promising potential in the fields of wearable electronics, healthcare monitoring, and human-machine interface applications.

Flexible Silica/MXene/Natural rubber film strain sensors with island chain structure for Healthcare monitoring.

The demand for flexible strain sensors with high sensitivity and durability has increased significantly. However, traditional sensors are limited in terms of their detection ranges and fabrications. In this work, a space stacking method was proposed to fabricate natural rubber (NR)/ Ti3C2Tx (MXene)/silica (SiO2) films that possessed exceptional electrical conductivity, sensitivity and reliability. The introduction of SiO2 into the NR/MXene composite enabled the construction of an "island-chain structure", which promoted the formation of conductive pathways and significantly improved the conductivity of the composite. Specifically, the electrical conductivity of the NR/MXene/10 wt%SiO2 composite was enhanced by about 200 times compared to that of the NR/MXene composite alone (from 0.07 to 13.4 S/m). Additionally, the "island-chain structure" further enhanced the sensing properties of the NR/MXene/10 wt%SiO2 composite, as evidenced by its excellent sensitivity (GF = 189.2), rapid response time (102 ms), and good repeatability over 10,000 cycles. The fabricated device demonstrates an outstanding mechanical sensing performance and can accurately detect human physiological signals. Specifically, the device serves as a strain detector, recognizing different strain signals by monitoring the movement of fingers, arms, and thighs. This study provides critical insights into composite manufacturing with exceptional conductivity, flexibility and stability, which are essential properties for creating high-performance flexible sensors.

Electrospun Nanofiber-Based Bioinspired Artificial Skins for Healthcare Monitoring and Human-Machine Interaction.

Artificial skin, also known as bioinspired electronic skin (e-skin), refers to intelligent wearable electronics that imitate the tactile sensory function of human skin and identify the detected changes in external information through different electrical signals. Flexible e-skin can achieve a wide range of functions such as accurate detection and identification of pressure, strain, and temperature, which has greatly extended their application potential in the field of healthcare monitoring and human-machine interaction (HMI). During recent years, the exploration and development of the design, construction, and performance of artificial skin has received extensive attention from researchers. With the advantages of high permeability, great ratio surface of area, and easy functional modification, electrospun nanofibers are suitable for the construction of electronic skin and further demonstrate broad application prospects in the fields of medical monitoring and HMI. Therefore, the critical review is provided to comprehensively summarize the recent advances in substrate materials, optimized fabrication techniques, response mechanisms, and related applications of the flexible electrospun nanofiber-based bio-inspired artificial skin. Finally, some current challenges and future prospects are outlined and discussed, and we hope that this review will help researchers to better understand the whole field and take it to the next level.