Healthcare Monitoring Applications Research Articles

Breathable Ti3C2Tx MXene/Protein Nanocomposites for Ultrasensitive Medical Pressure Sensor with Degradability in Solvents.

Flexible, breathable, and degradable pressure sensors with excellent sensing performance are drawing tremendous attention for various practical applications in wearable artificial skins, healthcare monitoring, and artificial intelligence due to their flexibility, breathability, lightweight, decreased electronic rubbish, and environmentally friendly impact. However, traditional plastic or elastomer substrates with impermeability, uncomfortableness, mechanical mismatches, and nondegradability greatly restricted their practical applications. Therefore, the fabrication of such pressure sensors with high flexibility, facile degradability, and breathability is still a critical challenge and highly desired. Herein, we present a wearable, breathable, degradable, and highly sensitive MXene/protein nanocomposites-based pressure sensor. The fabricated MXene/protein-based pressure sensor is assembled from a breathable conductive MXene coated silk fibroin nanofiber (MXene-SF) membrane and a silk fibroin nanofiber membrane patterned with a MXene ink-printed (MXene ink-SF) interdigitated electrode, which can serve as the sensing layer and the electrode layer, respectively. The assembled pressure sensor exhibits a wide sensing range (up to 39.3 kPa), high sensitivity (298.4 kPa-1 for 1.4-15.7 kPa; 171.9 kPa-1 for 15.7-39.3 kPa), fast response/recovery time (7/16 ms), reliable breathability, excellent cycling stability over 10 000 cycles, good biocompatibility, and robust degradability. Furthermore, it shows great sensing performance in monitoring human psychological signals, acting as an artificial skin for the quantitative illustration of pressure distribution, and wireless biomonitoring in real time. Considering the biodegradable and breathable features, the sensor may become promising to find potential applications in smart electronic skins, human motion detection, disease diagnosis, and human-machine interaction.

Fabrication of microfluidic channel of polydimethylsiloxane using X-ray lithography and its surface nanostructuring

The microfluidic devices have attracted considerable attention for their wide range of applications in healthcare, disease diagnosis, and environmental monitoring. We present the fabrication technique, surface wetting, and bonding of a polydimethylsiloxane (PDMS) microfluidic device that will be used as an electroosmotic micromixerfor biomolecules. This technique essentially requires micromold preparation and casting of PDMS. The hardened mold was fabricated on SU-8 using X-ray lithography (XRL) beamline, BL-7, Indus-2 as the synchrotron radiation source at Raja Ramanna Centre for Advanced Technology(RRCAT). The PDMS casting and thermal cross-linking was performed by spin-coating, followed by heating with specific thermocycle. This cross-linked PDMS was bonded with smooth surfaces that were treated with different reactive plasmas using a deep reactive-ion etching (DRIE) system. In a micro fluidic channel, the flow is usually a highly ordered laminar flow and due to lack of turbulence the mixing is very difficult for larger molecules such as peptides, proteins and high-molecular-weight nucleic acids. Here, we propose a microscale mixing device where active mixers are moved by external forces, such as an applied electric field. The dimensions of the fabricated device were generated through computer simulation using the finite-element based COMSOL Multiphysics 5. 4 software. The hydrophobic nature of PDMS hinders the mobility of biomolecules through the microchannel. In this work, plasma-induced surface wettability of PDMS with application of sulfur hexafluoride (SF6) and oxygen (O2) gas recipes was investigated. As a result, the SF6 plasma–treated microchannels became stable hydrophilic and exhibited an increased adhesion or reduced air-bubble trapping during filling with aqueous solutions.

Highly Sensitive On-Skin Temperature Sensors Based on Biocompatible Hydrogels with Thermoresponsive Transparency and Resistivity.

The development of electrically responsive sensors that interact directly with human skin and at the same time produce a visual indication of the temperature is in great demand. Here, we report a highly sensitive electronic skin (E-skin) sensor that measures and visualizes skin temperature simultaneously using a biocompatible hydrogel displaying thermoresponsive transparency and resistivity resulting from a temperature dependence of the strength of the hydrogen bonding between its components. This thermoresponsive hydrogel (TRH) showed a temperature dependence of not only the proton conductivity but also of its transmittance of light through a change in polymer conformation. We were able to use our TRH temperature sensor (TRH-TS) to measure temperature in a wide range of temperatures based on a change in its intrinsic resistivity (-0.0289°C-1 ) and to visualize the temperature due to its thermoresponsive transmittance (from 7% to 96%). The TRH-TS exhibited high reliability upon multiple cycles of heating and cooling. The on-skin TRH-TS patch is also shown to successfully produce changes in its impedance and optical transparency as a result of changes in skin temperature during cardiovascular exercise. This work has shown that our biocompatible TRH-TS is potentially suitable as wearable E-skin for various emerging flexible healthcare monitoring applications.

Uniform pressure responses for nanomaterials-based biological on-skin flexible pressure sensor array

Nanomaterials-based flexible sensor array has been receiving tremendous interest for uses in the fields of wearable devices, healthcare monitoring, and biomedical application. However, flexible sensor technology, whose active sensing element is introduced by nanomaterials, presents common practical problems such as mismatch and deviation in integration for sensor array architecture. In particular, sensor elements in array that are attached to the skin and then experience different bending strains suffer from non-uniform pressure responses, thus being a significant obstacle to commercialization. To provide a solution to these problems, we here propose an electrical circuit method for obtaining uniform pressure responses in nanomaterials-based on-skin flexible pressure sensor array. The proposed method employed by cubic spline interpolation achieve a wide range of calibration for operation in sensor array. The uniform responses were confirmed in a 4 × 4 pressure sensor array with the piezo-resistive characteristics due to graphene nanoplatelet (GNP) used as active material. The calibrated responses are demonstrated on the flexible sensor array attached to a human arm having uneven surface curvatures on which each sensor in the array may experience different bending strain. This work has opened the way to give reliable responses in resistance for wearable electronic sensor array attached to the body despite different external stress, and is an approach that can be extended to various sensor technologies.

Sunlight‐Responsive Photothermochromic Fabric with Reversible Color Changing Based on Photothermal Conversion

Intelligent color‐changing textiles have attracted increasing attention due to their potential application in healthcare monitoring and artificial intelligence. To realize the active modulation of color via sunlight, the photothermochromic fabric that adjusts its color based on the photothermal conversion performance of the sunlight‐responsive heating layer under irradiation of solar is fabricated by double‐side coating. Depending on the light absorption at 350–2500 nm, the sunlight‐responsive photothermochromic fabric (SPTCF) exposed under the irradiation power density of 500 W m−2 for 4 min displays efficient photothermal conversion, resulting in the increase in temperature from room temperature (23.0 °C) to above 47.0 °C. Simultaneously, the color of SPTCF changes from blue to white and returns to blue once the light is turned off for 3 min, indicating the sensitive and reversible photothermochromic property of the SPTCF. Compared with the contrast layer, the sunlight‐responsive heating layer exhibits a rapid photothermal conversion speed and high optical contrast under the same irradiation condition. The heating/cooling curves show steady signals after mechanical deformations and 150 cycles, revealing the favorable durability of the SPTCF under the solar irradiation. The sensitive photothermochromic properties promote the application of the SPTCF in smart wearable fields.

Recent developments of emerging inorganic, metal and carbon-based nanomaterials for pressure sensors and their healthcare monitoring applications

Recently, flexible pressure sensors have gained substantial research interest in bioelectronics because they can monitor the conditions of various organs, enable early diagnosis of diseases, and provide precise medical treatment by applying them to various parts of the body. In particular, inorganic materials, metal and carbon-based materials are broadly used in novel structured pressure sensors from wearable devices to implantable devices. With the excellent electronic properties, distinctive morphologies, and remarkable mechanical and chemical stability of these materials, it is expected that these flexible pressure sensors can be the basis for new methods for human healthcare. This article covers an extensive review of the inorganic, metal and carbon-based flexible pressure sensor design strategies and sensing mechanisms studied in recent years for diverse applications such as tactile sensors, arterial pulse sensors, intracranial pressure sensors, intraocular pressure sensors, and bladder pressure sensors. Each section provides an overview by introducing the recent progress in flexible pressure sensors.

Unobtrusive Sensors for the Assessment of Older Adult's Frailty: A Scoping Review.

Ubiquity (devices becoming part of the context) and transparency (devices not interfering with daily activities) are very significant in healthcare monitoring applications for elders. The present study undertakes a scoping review to map the literature on sensor-based unobtrusive monitoring of older adults’ frailty. We aim to determine what types of devices comply with unobtrusiveness requirements, which frailty markers have been unobtrusively assessed, which unsupervised devices have been tested, the relationships between sensor outcomes and frailty markers, and which devices can assess multiple markers. SCOPUS, PUBMED, and Web of Science were used to identify papers published 2010–2020. We selected 67 documents involving non-hospitalized older adults (65+ y.o.) and assessing frailty level or some specific frailty-marker with some sensor. Among the nine types of body worn sensors, only inertial measurement units (IMUs) on the waist and wrist-worn sensors comply with ubiquity. The former can transparently assess all variables but weight loss. Wrist-worn devices have not been tested in unsupervised conditions. Unsupervised presence detectors can predict frailty, slowness, performance, and physical activity. Waist IMUs and presence detectors are the most promising candidates for unobtrusive and unsupervised monitoring of frailty. Further research is necessary to give specific predictions of frailty level with unsupervised waist IMUs.

A brief review on integration between wireless sensor networks and Cloud

SummaryWireless sensor networks (WSNs) are an interconnection between hundreds to thousands of geographically distributed sensor nodes often called motes. WSNs are deployed in various fields for collecting data; they are used in healthcare monitoring, weather service, civil and military applications, and so on. WSNs are faced with many challenges due to their lack of computational resources, restrained storage capacities, and limited energy resources. The apparition of Cloud computing technology with its many advantages led the idea of integration between these two technologies. In this article, we will present an overview of works related to the interconnection between WSNs to the Cloud. After analyzing the contents of the published works, we established their classification according to the subjects treated. Four items are therefore selected, namely storage and computation, security, application and finally integration for Internet of Things (IoT). This brief review of the literature shows that the development of WSNs‐Cloud interconnection solves the problem of minimizing energy consumption at the sensor level, the problem of data storage, as well as the problem of access to the medium.

Hybrid Energy Harvester for Medical Sensor Node toward Real-Time Healthcare Monitoring

In healthcare applications, the remote monitoring for moving patients depends on medical sensor nodes, which should be mobile. Thus, the power mains of medical sensor nodes should be disconnected most of the time to monitor natural movements of patients. In this paper, a self-sustainable medical sensor node is proposed for healthcare monitoring applications. The node implementation consists of a microcontroller unit (MCU), a photo-plethysmography (PPG) sensor, a Bluetooth low energy (BLE) module, and a MPU module that includes a gyroscope with accelerometer. The power supply of the node is a hybrid energy harvester developed to provide a sustainable energy for the sensor node. The harvester is composed of a photovoltaic (PV) panel, a thermoelectric generator (TEG) module, a DC-DC converter, and a super-capacitor. Experimental results illustrate that the proposed node can monitor a physiological data on a mobile device using the BLE Terminal application.

A QRS-Detection Algorithm for Real-Time Applications

The QRS detection algorithm is substantial for healthcare monitoring and diagnostic applications. A low error detection without adding more computation is a big challenge for researchers. The proposed QRS detection algorithm is a simple, real-time, and high-performance hybrid technique based on decision tree and artificial neural networks (ANN). In this study, the five stages algorithm is designed, implemented, and evaluated for wearable healthcare applications. The first stage is filtering the original ECG signal to reduce the noise and baseline wandering. After that, a maximum or minimum moving-window for positive or negative peaks respectively is searching R-peaks for any expected value and finding the Q and S corresponding to this R-peak. Only these values from all ECG samples are passed to the next stage for feature extraction to reduce the algorithm computation. Stage four is excluded any unlikely points using the mean of the slope and level based on a simple decision tree. Finally, artificial neural networks are designed to classify the rest point for QRS detection using ANNs for each peak polarity to improve the network’s performance by separating the data as a positive or negative peak. The algorithm is evaluated based on MATLAB using the MIT-BIH Arrhythmia Database, and the results show a low error rate detection of 0.25%, high sensitivity of 99.86%, and high predictivity of 99.89%. We develop a new approach for real-time QRS detection with low resources and high efficiency compared with other approaches.

On Cellular Networks Supporting Healthcare Remote Monitoring in IoT Scenarios

In the next few years, fundamental technological transitions are expected both for wireless communications, soon resulting in the 5G era, and for the kind of pervasiveness that will be achieved thanks to the Internet of Things. The implementation of such new communication paradigms is expected to significantly revolutionize people’s lives, industry, commerce, and many daily activities. Healthcare applications are considered to be one of the most impacted industries. Sadly, in relation to the COVID-19 pandemic currently afflicting our society, health remote monitoring has become a fundamental and urgent application. The overcrowding of hospitals and medical facilities due to COVID-19, has unavoidably created delays and key issues in providing adequate medical assistance. In several cases, asymptomatic or light symptomatic COVID-19 patients have to be continuously monitored to prevent emergencies, and such an activity does not necessarily require hospitalization. Considering this research direction, this paper investigates the potentiality of cloud-based cellular networks to support remote healthcare monitoring applications implemented in accordance with the IoT paradigm, combined with future cellular systems. The idea is to conveniently replace the physical interaction between patients and doctors with a reliable virtual one, so that hospital services can be reserved for emergencies. Specifically, we investigate the feasibility and effectiveness of remote healthcare monitoring by evaluating its impact on the network performance. Furthermore, we discuss the potentiality of medical data compression and how it can be exploited to reduce the traffic load.

Ultrahigh-Sensitive Compression-Stress Sensor Using Integrated Stimuli-Responsive Materials.

Measurement of mechanical stresses, such as compression, shear, and tensile stresses, contributes toward achieving a safer and healthier life. In particular, the detection of weak compression stresses is required for healthcare monitoring and biomedical applications. Compression stresses in the order of 106 -1010 Pa have been visualized and/or quantified using mechano-responsive materials in previous works. However, in general, it is not easy to detect compression stresses weaker than 103 Pa using conventional mechano-responsive materials because the dynamic motion of the rigid mechano-responsive molecules is not induced by such a weak stress. In the present work, weak compression stresses in the order of 100 -103 Pa are visualized and measured via the integration of stimuli-responsive materials, such as layered polydiacetylene (PDA) and dry liquid (DL), through response cascades. DLs consisting of liquid droplets covered by solid particles release the interior liquid and collapse with application of a weak compression stress. The color of the layered PDA is changed by the spilled liquid as a chemical stress. A variety of weak compression stresses, such as expiratory pressure, are visualized and colorimetrically measured using the paper-based device of the integrated stimuli-responsive materials. Diverse mechano-sensing devices can be designed via the integration of stimuli-responsive materials.

A fully integrated wearable electronic device with breathable and washable properties for long-term health monitoring

The fully integrated wearable electronics have recently found widespread applications in long-term health monitoring, where one major challenge is to develop substrate materials with excellent breathability and washability. In this work, we developed a fully integrated wearable electronic device with breathable and washable properties through a facile and scalable paradigm. We directly mixed hydrophilic graphene oxide with hydrophilic cellulose fibers to ensure that graphene nanosheets uniformly spread all over the paper matrix. Then laser reduction was applied to fabricate a graphene-cellulose temperature sensor (LRG-TS), and thermal reduction to make a graphene-cellulose pressure sensor (TRG-PS), both of which were then connected with a flexible printed circuit board (FPCB) to form a fully integrated wearable electronic device with results readable by a mobile phone through Bluetooth. The excellent breathability, attributed to the unique 3D hierarchical porous structure of the e-paper, lays a solid foundation for long-term monitoring as demonstrated in rabbit skin in vitro. Besides, it can endure long-term soaking in water and multiple washing-drying cycles without compromising structural integrity and functional performance, as shown by sensing of body temperature and human motion. The developed device with these two unparalleled properties holds great promise for versatile wearable electronics applications in personal healthcare monitoring.

Thin, soft, skin-integrated foam-based triboelectric nanogenerators for tactile sensing and energy harvesting

Flexible and stretchable triboelectric nanogenerator (FS-TENG) is an excellent candidate of energy harvesters and tactile sensors as which can collect and induce electrical signals during daily activities and thus for self-powering wearable electronics. Here, ultrathin, soft, skin-integrated self-powering sensors based on FS-TENGs with sets of materials of porous poly(dimethylsiloxane) foam and advanced serpentine silver nanowires are reported to applicate TENG in high-sensitive human–machine interfaces. Systematically, studies of morphology and microstructures in the foam-based FS-TENGs indicate that appropriately designed thin foam triboelectric layers can effectively facile electrostatic induction and significantly enhance the electrical output signals. As a result, an open circuit voltage and a power density as high as 78.7 V and 33.75 W/m2 can be achieved, that is 20 times greater than the pure silicone-based FS-TENGs. Demonstrations of these FS-TENGs with the simple processing routes that associate with 24 sensors integrated on a glove and a large area 8 × 8 tactile sensor array highlight the capabilities of self-powering sensing and energy harvesting. These results offer an effective approach for thin, light wearable self-powering electronics for applications in healthcare monitoring and human-machine interfaces.

Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation.

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.

Flexible piezo-resistive pressure sensor based on conducting PANI on paper substrate

Green electronics is a trending and cost-effective method with potential application in health care monitoring and wearable electronics. We present a piezo resistive wearable pressure sensor using Polyaniline (PANI) incorporated with cellulose paper (CP) substrate. The ultralow-cost sensor is prepared by coating the sensing material on the paper using simple dip coating method. On stacking multiple PANI-CPs one upon another, the obtained flexible sensor demonstrates excellent sensitivity of 2.23 kPa−1, fast response of 70 ms and wide working range from 2 to 90 kPa. Furthermore, the sensor manifest excellent flexibility, outstanding stability up to 800 loading–unloading cycles and biodegradability. In addition, the pressure sensor can also be used for detecting rapid finger tapping. Apart from the other pressure sensors, this work mainly focusses on fabricating wearable piezo resistive sensors with low cost, facile fabricating process and shows potential for future applications in flexible wearable sensors.

Edge-Based Hybrid System Implementation for Long-Range Safety and Healthcare IoT Applications

In many conventional Internet-of-Things (IoT) applications, data are transferred directly from the sensor network to the cloud via a gateway for further data processing. However, this typical usage of a gateway is not suitable for every application. For short-range IoT wireless protocols [e.g., Bluetooth low energy (BLE)], multiple gateways are required to achieve broader coverage, which is inconvenient. In this article, an edge-based hybrid network system architecture is presented. The proposed system consists of hybrid routers and an IoT gateway. The router supports two wireless protocols, BLE and long range (LoRa), and is equipped with a solar energy harvester to extend the router's lifetime. It can extend the coverage of short-range BLE network by utilizing LoRa wireless technology, and support fundamental edge computing tasks such as preliminary data processing. The IoT gateway can support multiple IoT protocols, including LoRa, BLE, and XBee. It can perform more advanced edge computing tasks, such as data filtering, storage, processing, user interface, and cloud connection. Three case studies incorporating a wearable safety monitoring sensor network, a healthcare monitoring application, and a smart hospital application are studied with the proposed edge network system to demonstrate its promising capabilities to support IoT applications. Experimental evaluations indicate that by processing data at the edge, the minimal delay is only 11.5 ms. Furthermore, with the hybrid LoRa network implementation, the BLE network can be extended to 2.4 km.

Multi-functional and flexible helical fiber sensor for micro-deformation detection, temperature sensing and ammonia gas monitoring

Flexible and wearable strain sensors with multi-functionalities have aroused tremendous attention for various applications in disease diagnostics, environment detection and healthcare monitoring, etc. However, limited electromechanical performance and complicated fabrication process of strain sensors restrict their extensive applications in full-range stimuli monitoring. Herein, we fabricated flexible and wearable strain sensors, consisting of a robust fiber with helical structure and two polyaniline (PANI) layers bridged by reduced graphene oxide (rGO) layer through in-situ polymerization and ultrasonication. The flexible and wearable sensor possesses wide healthcare monitoring range (0.1%–200% strain), high sensibility especially for subtle deformation, excellent response stability, fast response/recovery time (100 ms/100 ms) and favorable durability. Meanwhile, our conductive PANI-rGO-PANI fiber (CPGPF) is assembled as a wearable sensor with the capability of precisely monitoring human body motions, temperature fluctuation and poisonous gas, indicating a promising multifunctional application in human-machine interaction and environment monitoring.

Accelerometer-based human activity recognition using 1D convolutional neural network

Human activity recognition (HAR) is an important research field with a variety of applications in healthcare monitoring, fitness tracking and in user-adaptive systems in smart environments. The performance of the activity recognition system is highly dependent on the features extracted from the sensor data which makes the selection of appropriate features a very important part of HAR. A 1D CNN model trained on accelerometer data is suggested in the paper for automatic feature extraction in a HAR system. A semi-automatic approach is used that effectively and efficiently determines the number of convolutional layers in the network, the number of kernels and the size of the kernels. The experimental results show that the suggested model outperforms several existing recognition approaches that use the same data set.

Design, Measurements, and Analysis of Enhanced Bandwidth UWB

Ambient computing enabled for body area networks have received much consideration over the past couple of years due to its applications in biomedical, healthcare monitoring, and military systems. Such systems are attracting users in other applications like gaming, fitness, sports, and other life style tools. The fast-moving lifestyle of people impelled them in a situation of less consideration of their health and sports, and it generated the need of healthcare monitoring and tracking devices. Advances in wireless technology and embedded technology have generated keen interest in antenna, mounted on or around the body to transmit or receive the vital data of human body to on-body or off-body systems. The proposed antenna is for the use in wireless body area network (WBAN), in UWB frequency range 3.1 GHz.-10.6 GHz. Enhanced bandwidth ultra wideband (EB-UWB) patch antenna consists of the dimensions 30 mm×27 mm×1.6 mm. The prototype is fabricated and tested in free space and on-body scenario.