Continuous Healthcare Monitoring Research Articles

High‐Resolution Intaglio Transfer Printing of Silver Nanowires for Wearable Electrophysiological Sensors

Silver nanowires (NWs) are promising materials for flexible electronics, such as electronic skins due to their excellent electrical, thermal, and mechanical properties. Achieving precise patterning of Ag NWs is essential for the successful integration and miniaturization of the electronic device system, but the high aspect ratio (AR) of NWs and the high porosity of NW networks pose challenges in forming high‐resolution patterns. Herein, the intaglio transfer printing technique to create high‐resolution patterning of ultralong Ag NWs (AR≈1000) is presented. During the pattern formation process, the external force becomes concentrated specifically at the edge of the intaglio trench, resulting in the breaking of the entangled Ag NW network in the corresponding region. This simple yet effective technique enables precise high‐resolution (minimum line width: 7 µm) and complicated Ag NW patterns on flexible substrates. The patterned Ag NWs are conformally attached to the various curvilinear surfaces and show high mechanical stability under continuous bending conditions. Wearable electrophysiological sensors are demonstrated to monitor electromyography and electrocardiogram signals in real‐time for continuous healthcare monitoring. This patterning strategy offers an effective approach for achieving high‐resolution patterns of highly anisotropic nanomaterials and highlights the potential of patterned Ag NWs in wearable electronics.

Recent advancements in machine learning enabled portable and wearable biosensors

Recent advances in noninvasive portable and wearable biosensors have attracted significant attention due to their capability to offer continual physiological information for continuous healthcare monitoring through the collection of biological signals. To make the collected biological data understandable and improve the efficacy of these biosensors, scientists have integrated machine learning (ML) with biosensors to analyze large sensing data through various ML algorithms. In this article, we have highlighted the recent developments in ML-enabled noninvasive biosensors. Initially, we introduced and discussed the basic features of ML algorithms used in data processing to build an intelligent biosensor system and the capability to make clinical decisions. Next, the principles of portable and wearable biosensors, the application of different ML models in diverse biosensors for healthcare applications, and their impact on the performance of biosensors are discussed. The last section highlights the challenges (such as data privacy, consistency, stability, accuracy, scalable production, and adaptive learning capacity), future prospects, and necessary steps required to address these issues, spotlighting their revolutionizing impact on the healthcare industry for the development of next-generation ML-enabled efficient biosensors.

A new paradigm of reliable sensing with field-deployed electrochemical sensors integrating data redundancy and source credibility

For a continuous healthcare or environmental monitoring system, it is essential to reliably sense the analyte concentration reported by electrochemical sensors. However, environmental perturbation, sensor drift, and power-constraint make reliable sensing with wearable and implantable sensors difficult. While most studies focus on improving sensor stability and precision by increasing the system’s complexity and cost, we aim to address this challenge using low-cost sensors. To obtain the desired accuracy from low-cost sensors, we borrow two fundamental concepts from communication theory and computer science. First, inspired by reliable data transmission over a noisy communication channel by incorporating redundancy, we propose to measure the same quantity (i.e., analyte concentration) with multiple sensors. Second, we estimate the true signal by aggregating the output of the sensors based on their credibility, a technique originally developed for “truth discovery” in social sensing applications. We use the Maximum Likelihood Estimation to estimate the true signal and the credibility index of the sensors over time. Using the estimated signal, we develop an on-the-fly drift-correction method to make unreliable sensors reliable by correcting any systematic drifts during operation. Our approach can determine solution pH within 0.09 pH for more than three months by detecting and correcting the gradual drift of pH sensors as a function of gamma-ray irradiation. In the field study, we validate our method by measuring nitrate levels in an agricultural field onsite over 22 days within 0.06 mM of a high-precision laboratory-based sensor. We theoretically demonstrate and numerically validate that our approach can estimate the true signal even when the majority (~ 80%) of the sensors are unreliable. Moreover, by restricting wireless transmission to high-credible sensors, we achieve near-perfect information transfer at a fraction of the energy cost. The high-precision sensing with low-cost sensors at reduced transmission cost will pave the way for pervasive in-field sensing with electrochemical sensors. The approach is general and can improve the accuracy of any field-deployed sensors undergoing drift and degradation during operation.

Press-N-Go On-Skin Sensor with High Interfacial Toughness for Continuous Healthcare Monitoring.

On-skin electronic sensors are demanded for healthcare monitoring such as the continuous recording of biopotential and motion signals from patients. However, the mechanical mismatches and poor interface adhesion at the skin/sensor interfaces always cause high interfacial impedance and artifacts, frequent interfacial failure, and unexpected depletion of the device, which significantly limit the performance of the sensors. We here develop an on-skin sensor based on a conductive pressure-sensitive tape, which is assembled from supramolecular dual-cross-linked hydrogel composites. Both covalent and noncovalent cross-links in the hydrogel networks could harvest high flexibility, pressure-sensitive adhesion, and high interfacial toughness altogether, enabling a convenient "Press-N-Go" application of the sensor on human skin without additional pre/post-treatment on the skin or the senor. The high conformability and low resistivity of the tape can sustainably lower the interfacial impedance and thus improve signal quality in various measurement conditions. Our design provides a feasible path to develop interface-toughened on-skin electronics, which is desired in dynamic human-machine interfaces.

A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services.

Wireless body area network (WBAN) is widely adopted in healthcare services, providing remote real-time and continuous healthcare monitoring. With the massive increase of detective sensor data, WBAN is largely restricted by limited storage and computation capacity, resulting in severely decreased efficiency and reliability. Mobile edge computing (MEC) technique can be combined with WBAN to resolve this issue. This paper studies the joint optimization problem of computational offloading and resource allocation (JCORA) in MEC for healthcare service scenarios. We formulate JCORA as a Markov decision process and propose a deep deterministic policy gradient-based WBAN offloading strategy (DDPG-WOS) to optimize time delay and energy consumption in interfered transmission channels. This scheme employs MEC to mitigate the computation pressure on a single WBAN and increase the transmission ability. Further, DDPG-WOS optimizes the offloading strategy-making process by considering the channel condition, transmission quality, computation ability and energy consumption. Simulation results verify the effectiveness of the proposed optimization schema in reducing energy consumption and computation latency and increasing the utility of WBAN compared to two competitive solutions.

A laser-scribed wearable strain sensing system powered by an integrated rechargeable thin-film zinc-air battery for a long-time continuous healthcare monitoring

Wearable sensors with long cruising times without the need for an external wire-connected power supply are urgently required for a continuous physiological signal monitoring. Herein, we report the design and fabrication of a wearable all-in-one sensing system that integrates a sensitive strain sensor and a rechargeable zinc-air battery on the same laser-induced graphene (LIG) platform. The sensing electrode of the strain sensor, the catalytic air electrode of the zinc-air battery and the interconnection lines between the two are simultaneously fabricated through the same laser-scribing process on polyimide (PI) followed by pattern transfer to a flexible and stretchable polydimethylsiloxane (PDMS) substrate. In the sensor, the conductive porous LIG exhibits a high gauge factor (~2710.95), a wide detection range (~40%), and a well cyclic loading-unloading stability (>20,000 cycles). In the zinc-air battery, Co3O4 nanoparticles are in-situ laser-sintered on LIG to form an excellent electrode/electrolyte/air three-phased interfacing structure for a high catalytic performance, and a moisturized tetraethyl ammonium hydroxide (TEAOH)-KOH/polyvinyl alcohol (PVA) gel electrolyte tightly binds the Co3O4/LIG air electrode and zinc foil electrode together into an integrate thin-film energy device, which provides a high open-circuit voltage (~1.39 V) and a high specific capacity (712 mAh g−1 at 0.2 mA cm−2). For a proof-of-concept, an intelligent wristband based on the sensing system was fabricated and worn on the wearer’s wrist for a long-time continuous pulse monitoring of up to 10 h. The health state of the wearer can be depicted upon the acquired pulse waveforms. The developed LIG-based sensing system with a high internal powering capability will be promising in wearable sensing applications.

Multi-Watt-Level 4.9-GHz Silicon Power Amplifier for Portable Thermoacoustic Imaging

Microwave-induced thermoacoustic (TA) imaging, combining high microwave contrast with high ultrasonic resolution has the potential to revolutionize applications such as continuous healthcare monitoring, point-of-care imaging, and biometric authentication. However, the size, cost, and integration of a high-power microwave transmitter is a key bottleneck in making TA imaging truly portable, affordable, and ubiquitous. Toward that end, this work presents a compact 4.9-GHz pulsed power amplifier (PA) with a 4.87-mm² active area implemented in a 55-nm BiCMOS technology, operating in a duty-cycled mode and achieving 37.3-dBm peak output power—the highest demonstrated peak power in PAs fabricated on a silicon substrate with deep submicron CMOS integration. We also reconstruct the first known high-fidelity TA images of tissue phantoms using an integrated silicon PA.

Wearable Respiration Sensor for Continuous Healthcare Monitoring Using a Micro‐Thermoelectric Generator with Rapid Response Time and Chip‐Level Design

AbstractThermoelectric generators (TEG) serve as excellent passive wearable sensors for monitoring human body heat. However, a micro‐TEG (μTEG) with chip‐level size, rapid response, and high and stable responsivity is desired for real‐time and full‐time respiration monitoring to predict and diagnose breath‐related diseases. In this study, a thin‐film compact μTEG is elaborately designed by combining an ultrathin vertical structure for rapid heat conduction and a horizontal high‐integration density for transient response and a high filling rate. The device integrated with 28‐pair micro thermoelectric (TE) legs is fabricated on an aluminum nitride (AlN) substrate, which is patterned using ultrafast laser direct writing with embedded bottom contacts and TE legs. This unique design of the proposed μTEG provides a rapid response of 8 ms and chip‐level size of 1.9 mm × 2.7 mm × 400 μm for easy wearability. Additionally, application scenarios of real‐time respiration monitoring are demonstrated by mounting the μTEG under the nostril and near the mouth. The recorded airflow signals are displayed precisely with distinct features separating the nose and mouth breathing. Thus, the study presents a subtle and wearable respiration sensor for real‐time and full‐time human physiological signal acquisition.

Garment embedded sweat-activated batteries in wearable electronics for continuous sweat monitoring

Thin, soft, and skin-integrated electronic system has great advantages for realizing continuous human healthcare monitoring. Here, we report an ultra-thin, flexible, and garment-based microelectronics powered by sweat-activated batteries (SABs) and applications of powering biosensors and microelectronic systems for real time sweat monitoring. The SAB cell is ultra-thin (1.25 mm) with excellent biocompatibility. The SAB has good electricity output with high capacity (14.33 mAh) and maximum power density (3.17 mW cm−2) after being activated by the sweat volume of 0.045 mL cm−2, which could continuously power 120 light emitting diodes over 3 h. The outputs could maintain stable after repeatable bending. Wireless microelectronics system could be continuously powered by the SABs for 3 h to monitor sweat and physiological information, including sweat Na+ concentration, pH, and skin impedance. The reported integrated system provides a potential for solving the power issues of flexible wearable electronics and realizing personalized medicine.

A Laser-Scribed Wearable Strain Sensing System Powered by an Integrated Rechargeable Thin-Film Zinc-Air Battery for a Long-Time Continuous Healthcare Monitoring

A Laser-Scribed Wearable Strain Sensing System Powered by an Integrated Rechargeable Thin-Film Zinc-Air Battery for a Long-Time Continuous Healthcare Monitoring

Microfluidic chip connected to porous microneedle array for continuous ISF sampling.

Minimally invasive biosensing using microneedles (MNs) is a desirable technology for continuous healthcare monitoring. Among a wide range of MNs, porous MNs are expected to be applied for sampling of interstitial fluids (ISF) by connecting the internal tissue to external measurement devices. In order to realize a continuous measurement of biomarkers in ISF through porous MNs, their integration with a microfluidic chip is a promising approach due to its applicability to micro-total analysis system (μTAS) technology. In this study, we developed a fluidic system to directly interface porous MNs to a microfluidic chip consisting of a capillary pump for the continuous sampling of ISF. The porous and flexible MNs made of PDMS are connected to the microfluidic chip fabricated by standard microelectro-mechanical system (MEMS) processes, showing a continuous flow of phosphate buffered saline (PBS). The developed device will lead to the minimally invasive and continuous biosampling for long-term healthcare monitoring.

One-class autoencoder approach for optimal electrode set identification in wearable EEG event monitoring.

A limiting factor towards the wide use of wearable devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true in electroencephalography (EEG), where numerous electrodes are placed in contact with the scalp to perform brain activity recordings. In this work, we propose to identify the optimal wearable EEG electrode set, in terms of minimal number of electrodes, comfortable location and performance, for EEG-based event detection and monitoring. By relying on the demonstrated power of autoencoder (AE) networks to learn latent representations from high-dimensional data, our proposed strategy trains an AE architecture in a one-class classification setup with different electrode combinations as input data. The model performance is assessed using the F-score. Alpha waves detection is the use case through which we demonstrate that the proposed method allows to detect a brain state from an optimal set of electrodes. The so-called wearable configuration, consisting of electrodes in the forehead and behind the ear, is the chosen optimal set, with an average F-score of 0.78. This study highlights the beneficial impact of a learning-based approach in the design of wearable devices for real-life event-related monitoring.

Edge Intelligent Agent Assisted Hybrid Hierarchical Blockchain for continuous healthcare monitoring & recommendation system in 5G WBAN-IoT

Wireless Body Area Network (WBAN) is an emerging technology that faces many challenges for monitoring patient health state information and early diagnosis of any disease (e.g. COVID-19). In addition, WBAN has many issues such as high energy consumption, overhead and latency due to the scarcity in resources, for that we bring edge assisted 5G environment in WBAN. The patient information is sensitive and easily traced by the attackers due to insecurity, for that, we proposed the EiA-H2B model (Edge intelligent Agent Hybrid Hierarchical Blockchain) which includes five phases. Firstly we proposed Biological PUF based sensor authentication, which is a strong credential that provides high security. All the sensors are authenticated by the virtual authenticator that is deployed by a blockchain ledger. Due to the mobility of WBAN, we focused on handover that is done by Bi-Partite One to N matching theory. Secondly, we proposed high voting based virtual clustering, in this phase high voting sensor is elected as a super node and that is used to create a virtual cluster. Thirdly, we proposed duty cycle MAC scheduling protocol is used to predict the original state of the sensors which is done by Attention Matrix based Gated Recurrent Unit (AM-GRU), and then the timeslots are allocated for avoiding packet loss and retransmission. Fourth, we perform relay selection and routing that is done by Global Optimization based Artificial Electric Field Algorithm (AEFA). And finally, we predict emergency data and generate an alert in EiA using Human Learning aided State Action Reward State Action (SARSA) algorithm that finds the current state and predicts the corresponding action. Based on that current state information SARSA provides an efficient recommendation to the patients. Experiments are conducted by OMNeT ++ network simulator and evaluate the performance of the proposed EiA-H2B model in terms of energy consumption, success rate, network throughput, end to end delay, packet loss rate, authentication time and processing time.

Real-Time Monitoring System Based on Li-Fi Network Technology in Healthcare

Patients at healthcare facilities require a long-term continuous healthcare monitoring system to keep track of their vital signs. Because it deals with human life, this system must be safe, trustworthy, and ensure that it does not interfere with available radio frequencies or sensitive electronic devices such as MRI (magnetic resonance imaging). This paper introduces a patient monitoring system in intensive care that used Li-Fi technology, designed to help enhance patient care and boost doctor’s clinical results. This robust approach timely collects patient data and integrates securely within the hospital IT framework feeding information to physicians, allowing them to make informed clinical decisions. The system used real-time software which displays the data from different locations for assessment. It was successfully tested in the laboratory. Some measurements are discussed, which compare the received pulses to the modules line of sight (LOS) output channel to correlate the transmitted channels. In this work, experimental analysis and measurements are performed to check the efficiency of the proposed concept.

Cactus-Spine-Inspired Sweat-Collecting Patch for Fast and Continuous Monitoring of Sweat.

A sweat sensor is expected to be the most appropriate wearable device for noninvasive healthcare monitoring. However, the practical use of sweat sensors is impeded by irregular and low sweat secretion rates. Here, a sweat-collecting patch that can collect sweat efficiently for fast and continuous healthcare monitoring is demonstrated. The patch uses cactus-spine-inspired wedge-shaped wettability-patterned channels on a hierarchical microstructured/nanostructured surface. The channel shape, in combination with the superhydrophobic/superhydrophilic surface materials, induces a unidirectional Laplace pressure that transports the sweat to the sensing area spontaneously even when the patch is aligned vertically. The patch demonstrates superior sweat-collecting efficiency and reduces the time required to fill the sensing area by transporting sweat almost without leaving it inside the channel. Therefore, a sensor based on the patch responds quickly to biochemicals in sweat, and the patch enables the continuous monitoring of changes in sweat biochemicals according to their changes in the wearer's blood.

Stable Bioelectric Signal Acquisition Using an Enlarged Surface-Area Flexible Skin Electrode

Numerous wearable devices were developed to measure bioelectric signals for continuous healthcare monitoring. The electrode, which interconnects electronics and the human body, significantly affects the signal quality. Although Ag/AgCl electrodes have been commonly used, noble-metal electrodes are more promising in terms of long-term reusability and flexibility. However, the signal-to-noise ratio (SNR) of noble metals is still insufficient for highly accurate biosignal acquisition. In this study, we propose an approach to enhance the electrical characteristics of a noble-metal skin electrode by surface modification using gold nanoparticles. The process parameters for nanoparticle deposition were optimized to maximize the surface area, thereby significantly improving the SNR of the electrode. The SNR value was increased by 51% in electrocardiogram and by 63% in electromyogram (EMG). We also propose an approach to quantify the motion artifact by spectral analysis, and the high flexibility of our electrode reduced the motion noise by 95% compared to the conventional Ag/AgCl electrode. The enhanced electrode interface paves the way for analyzing complex biosignals such as EMG and electroencephalogram in wearable applications.

Electrochromic Alarm System with Computer Vision in Smart Contact Lens

Recent advances in smart healthcare system using IoT are becoming mandatory with continuous and real time healthcare monitoring system. This system can give signal to the user by sound or stimulation when health condition become severe. As one of them, smart contact lens can be used to monitor vital signals from glucose level of the tear related to diabetes1 and intraocular pressure related to glaucoma2. However, previous researches about smart contact lens used wireless communication for signal transfer which need external receiver and antenna inside. Moreover, research that deals with data transmission to the user by image visualization on the contact lens has not been yet published. Herein, we have demonstrated electrochromic alarm system which uses color change to give direct signal transfer to the user by electrochemical reaction with ions in tear. This system showed clear color change with curved shape in limited spaces. Also, diverse intensities and frequencies of color change were controlled by the various voltages and duration of voltage application to this system. In addition, various color changing patterns can be shown to the user by controlling the duration of voltage application and Morse code was used to transfer words to the user in specific purpose. With the help of computer vision, color changing patterns were detected and interpreted. We believe that our electrochromic alarm system with diverse patterns suggest new method for direct data transmission to the user with the help of computer vision. Also, it can be integrated into smart contact lens with battery and various sensors.References D. H. Keum, S.-K. Kim, J. Koo, G.-H. Lee, C. Jeon, J. W. Mok, B. H. Mun, K. J. Lee, E. Kamrani, C.-K. Joo, S. Shin, J.-Y. Sim, D. Myung, S. H. Yun, Z. Bao, S. K. Hahn, Wireless smart contact lens for diabetic diagnosis and therapy. Sci. Adv. 6, eaba3252 (2020).J. Kim, M. Kim, M.-S. Lee, K. Kim, S. Ji, Y.-T. Kim, J. Park, K. Na, K.-H. Bae, H. K. Kim, F. Bien, C. Y. Lee, J.-U. Park, Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics. Nat. Commun. 8, 14997 (2017). Figure 1

Identifying human body states by using a flexible integrated sensor

Flexible sensors are required to be lightweight, compatible with the skin, sufficiently sensitive, and easily integrated to extract various kinds of body vital signs during continuous healthcare monitoring in daily life. For this, a simple and low-cost flexible temperature and force sensor that uses only two carbon fiber beams as the sensing layer is reported in this work. This simple, flexible sensor can not only monitor skin temperature changes in real time but can also extract most pulse waves, including venous waves, from most parts of the human body. A pulse diagnostic glove containing three such flexible sensors was designed to simulate pulse diagnostic methods used in traditional Chinese medicine. Wearable equipment was also designed in which four flexible sensors were fixed onto different body parts (neck, chest, armpit, and fingertip) to simultaneously monitor body temperature, carotid pulse, fingertip artery pulse, and respiratory rate. Four important physiological indicators—body temperature (BT), blood pressure (BP), heart rate (HR), and respiratory rate (RR)—were extracted by the wearable equipment and analyzed to identify exercise, excited, tired, angry, and frightened body states.

Wearable Flexible Sensor Sheets toward Continuous Healthcare Monitoring

Wearable Flexible Sensor Sheets toward Continuous Healthcare Monitoring

Self-powered wearable pressure sensing system for continuous healthcare monitoring enabled by flexible thin-film thermoelectric generator

Flexible and portable pressure sensors with highly sensitive and cost-effective attributes are of great demand in wearable electronics, biomedical monitoring, and artificial intelligence. To realize portable pressure sensors with mobile operation, ultrathin, flexible, and sustainable power sources are highly desired. Here, a self-powered wearable pressure sensing system is developed by integrating a conductive elastomer-based pressure sensor with a flexible thin-film thermoelectric generator (tf-TEG) to harvest body heat. Laser engraving technique is adopted to achieve pressure sensor with tunable sensitivity through the controllable design of surface microstructures. To produce a reliable and renewable power supply for the pressure sensor, the flexible tf-TEG is constructed with high thermal-conductivity insulation films for heat absorbing and flexible hydrogels as heat sink, resulting in a large temperature gradient for high power generation. In particular, the pressure sensitivity of the self-powered system is independent of the temperature gradient across the tf-TEG, allowing it to maintain a stable performance under various temperature differences between human skin and surroundings. For the first time, the self-powered pressure sensing system proposed in this work enables continuous monitoring of human physiological signals and body motions entirely powered by the skin-attachable tf-TEG without an electronic boost circuitry. A self-powered wearable pressure sensing system is developed by integrating a flexible thin- film thermoelectric generator (tf-TEG) with a pressure sensor. The tf-TEG was constructed with flexible heat absorbing and heat sink. Along with a low working-current pressure sensor with micro-cone surface, the self-powered sensing system is successfully applied to monitor various physiological signals for human healthcare, entirely powered by the skin-attachable tf-TEG without an electronic boost circuitry. • A self-powered pressure sensing system enabled by thin-film TE generator is reported. • The thermal design of TEG facilitates high voltage output by harvesting body heat. • The sensitivity of pressure sensor is tunable by controlling surface microstructure. • The proposed sensing system can continuously detect human physiological signals.