Electric Double Layer Based Epidermal Electronics for Healthcare and Human-Machine Interface

Epidermal electronics, an emerging interdisciplinary field, is advancing the development of flexible devices that can seamlessly integrate with the skin. These devices, especially Electric Double Layer (EDL)-based sensors, overcome the limitations of conventional electronic devices, offering high sensitivity, rapid response, and excellent stability. Especially, Electric Double Layer (EDL)-based epidermal sensors show great potential in the application of wearable electronics to detect biological signals due to their high sensitivity, fast response, and excellent stability. The advantages can be attributed to the biocompatibility of the materials, the flexibility of the devices, and the large capacitance due to the EDL effect. Furthermore, we discuss the potential of EDL epidermal electronics as wearable sensors for health monitoring and wound healing. These devices can analyze various biofluids, offering real-time feedback on parameters like pH, temperature, glucose, lactate, and oxygen levels, which aids in accurate diagnosis and effective treatment. Beyond healthcare, we explore the role of EDL epidermal electronics in human-machine interaction, particularly their application in prosthetics and pressure-sensing robots. By mimicking the flexibility and sensitivity of human skin, these devices enhance the functionality and user experience of these systems. This review summarizes the latest advancements in EDL-based epidermal electronic devices, offering a perspective for future research in this rapidly evolving field.

Ultrasensitive Linear Capacitive Pressure Sensor with Wrinkled Microstructures for Tactile Perception

Ultrasensitive flexible pressure sensors with excellent linearity are essential for achieving tactile perception. Although microstructured dielectrics have endowed capacitive sensors with ultrahigh sensitivity, the compromise of sensitivity with increasing pressure is an issue yet to be resolved. Herein, a spontaneously wrinkled MWCNT/PDMS dielectric layer is proposed to realize the excellent sensitivity and linearity of capacitive sensors for tactile perception. The synergistic effect of a high dielectric constant and wrinkled microstructures enables the sensor to exhibit linearity up to 21 kPa with a sensitivity of 1.448 kPa^-1 and a detection limit of 0.2 Pa. Owing to these merits, the sensor monitors subtle physiological signals such as various arterial pulses and respiration. This sensor is further integrated into a fully multimaterial 3D-printed soft pneumatic finger to realize material hardness perception. Eight materials with different hardness values are successfully discriminated, and the capacitance of the sensor varies linearly (R² > 0.975) with increasing hardness. Moreover, the sensitivity to the material hardness can be tuned by controlling the inflation pressure of the soft finger. As a proof of concept, the finger is used to discriminate pork fats with different hardness, paving the way for hardness discrimination in clinical palpation.

Flexible Wearable Sensors in Medical Monitoring

The popularity of health concepts and the wave of digitalization have driven the innovation of sensors in the medical field. Such continual development has made sensors progress in the direction of safety, flexibility, and intelligence for continuous monitoring of vital signs, which holds considerable promise for changing the way humans live and even treat diseases. To this end, flexible wearable devices with high performance, such as high sensitivity, high stability, and excellent biodegradability, have attracted strong interest from scientists. Herein, a review of flexible wearable sensors for temperature, heart rate, human motion, respiratory rate, glucose, and pH is highlighted. In addition, engineering issues are also presented, focusing on material selection, sensor fabrication, and power supply. Finally, potential challenges facing current technology and future directions of wearable sensors are also discussed.

Flexible Pressure Sensors with Combined Spraying and Self-Diffusion of Carbon Nanotubes

High-performance wearable sensors are required for applications in medical health and human-machine interaction, but their application has limited owing to the trade-off between sensitivity, pressure range, and durability. Herein, we propose the combined spraying and self-diffusion process of carbon nanotubes (CNTs) to balance and improve these parameters with the CNTs spontaneously diffusing into the film surface before the film curing. The obtained sensor not only achieves high sensitivity (155.54 kPa^-1) and ultrawide pressure detection range (0.1-500 kPa) but also exhibits exceptional durability (over 12,000 pressure cycles at a high pressure of 300 kPa). In addition, the sensor exhibits a fast response (25 ms), good stability, and full flexibility. This process is a general approach that may improve the performance of various types of thin film piezoresistive sensors. Besides, the fabricated sensors can be flexibly scaled into sensor arrays and communicate with smart devices to achieve wireless smart monitoring. At present, the sensor shows broad application prospects in the fields of intelligent medical health and motion sensing.

All-Nanofibrous Ionic Capacitive Pressure Sensor for Wearable Applications

Currently, with the development of electronic skins (e-skins), wearable pressure sensors with low energy consumption and excellent wearability for long-term physiological signal monitoring are urgently desired but remain a challenge. Capacitive-type devices are desirable candidates for wearable applications, but traditional capacitive pressure sensors are limited by low capacitance and sensitivity. In this study, an all-nanofibrous ionic pressure sensor (IPS) is developed, and the formation of an electrical double layer at the electrode/electrolyte contact interface significantly enhances the capacitance and sensing properties. The IPS is fabricated by sandwiching a nanofibrous ionic gel sensing layer between two thermoplastic polyurethane nanofibrous membranes with graphene electrodes. The IPS has a high sensitivity of 217.5 kPa^-1 in the pressure range of 0-5 kPa, which is much higher than that of conventional capacitive pressure sensors. Combined with the rapid response and recovery speed (30 and 60 ms), the IPS is suitable for real-time monitoring of multiple physiological signals. Moreover, the nanofiber network endows the IPS with excellent air permeability and heat dissipation, which guarantees comfort during long-term wearing. This work provides a viable strategy to improve the wearability of wearable sensors, which can promote healthcare and human-machine interaction applications.

Design and Implementation of an Indoor Warning System with Physiological Signal Monitoring for People Isolated at Home

Due to the recent COVID-19 pandemic, many people have faced in-home isolation, as every suspected patient must stay at home. The behavior of such isolated people needs to be monitored to ensure that they are staying at home. Using a camera is a very practical method. However, smart bracelets are more convenient when personal privacy is a concern or when the blood oxygen value or heart rate must be monitored. In this study, a low-cost indoor positioning system that uses a Bluetooth beacon, a smart bracelet, and an embedded system is proposed. In addition to monitoring whether a person living alone is active in a specific environment and tracking the heart rate or blood oxygen value under particular conditions, this system can also send early warning signals to specific observation units or relatives through instant messaging software.

Noncontact Physiological Measurement Using a Camera: A Technical Review and Future Directions

Using a camera as an optical sensor to monitor physiological parameters has garnered considerable research interest in biomedical engineering in recent decades. Researchers have explored the use of a camera for monitoring a variety of physiological waveforms, together with the vital signs carried by these waveforms. Most of the obtained waveforms are related to the human respiratory and cardiovascular systems, and in addition of being indicative of overall health, they can also detect early signs of certain diseases. While using a camera for noncontact physiological signal monitoring offers the advantages of low cost and operational ease, it also has the disadvantages such as vulnerability to motion and lack of burden-free calibration solutions in some use cases. This study presents an overview of the existing camera-based methods that have been reported in recent years. It introduces the physiological principles behind these methods, signal acquisition approaches, various types of acquired signals, data processing algorithms, and application scenarios of these methods. It also discusses the technological gaps between the camera-based methods and traditional medical techniques, which are mostly contact-based. Furthermore, we present the manner in which noncontact physiological signal monitoring use has been extended, particularly over the recent years, to more day-to-day aspects of individuals' lives, so as to go beyond the more conventional use case scenarios. We also report on the development of novel approaches that facilitate easier measurement of less often monitored and recorded physiological signals. These have the potential of ushering a host of new medical and lifestyle applications. We hope this study can provide useful information to the researchers in the noncontact physiological signal measurement community.

Substrate-Free Multilayer Graphene Electronic Skin for Intelligent Diagnosis

Current wearable sensors are fabricated with substrates, which limits the comfort, flexibility, stretchability, and induces interface mismatch. In addition, the substrate prevents the evaporation of sweat and is harmful to skin health. In this work, we have enabled the substrate-free laser scribed graphene (SFG) electronic skin (e-skin) with multifunctions. Compared with the e-skin with the substrate, the SFG has good gas permeability, low impedance, and flexibility. Only assisted using water, the SFG can be transferred to almost any objects including silicon and human skin and it can even be suspended. Many through-holes like stomas in leaf can be formed in the SFG, which make it breathable. After designing the pattern, the gauge factor (GF) of graphene electronic skin (GES) can be designed as the strain sensor. Physiological signals such as respiration, human motion, and electrocardiogram (ECG) can be detected. Moreover, the suspended SFG detect vibrations with high sensitivity. Due to the substrate-free structure, the impedance between SFG e-skin and the human body decreases greatly. Finally, an ECG detecting system has been designed based on the GES, which can monitor the body condition in real time. To analyze the ECG signals automatically, a convolutional neural network (CNN) was built and trained successfully. This work has high potential in the field of health telemonitoring.

Surface Engineering of a 3D Topological Network for Ultrasensitive Piezoresistive Pressure Sensors

Polypyrrole (PPy) is a good candidate material for piezoresistive pressure sensors owing to its excellent electrical conductivity and good biocompatibility. However, it remains challenging to fabricate PPy-based flexible piezoresistive pressure sensors with high sensitivity because of the intrinsic rigidity and brittleness of the film composed of dense PPy particles. Here, a rational structure, that is, 3D-conductive and elastic topological film composed of coaxial nanofiber networks, is reported to dramatically improve the sensitivity of flexible PPy-based sensors. The film is prepared through surface modification of electrospun polyvinylidene fluoride (PVDF) nanofibers by polydopamine (PDA), in order to homogeneously deposit PPy particles on the nanofiber networks with strong interfacial adhesion (PVDF/PDA/PPy, PPP). This unique structure has a high surface area and abundant contact sites, leading to superb sensitivity against a subtle pressure. The as-developed piezoresistive pressure sensor delivers a low limit of detection (0.9 Pa), high sensitivity (139.9 kPa^-1), fast response (22 ms), good cycling stability (over 10,000 cycles), and reliability, thereby showing a promising value for applications in the fields of health monitoring and artificial intelligence.

Wearable Capacitive Pressure Sensor Based on MXene Composite Nanofibrous Scaffolds for Reliable Human Physiological Signal Acquisition

In recent years, highly sensitive pressure sensors that are flexible, biocompatible, and stretchable have attracted significant research attention in the fields of wearable electronics and smart skin. However, there has been a considerable challenge to simultaneously achieve highly sensitive, low-cost sensors coupled with optimum mechanical stability and an ultralow detection limit for subtle physiological signal monitoring devices. Targeting aforementioned issues, herein, we report the facile fabrication of a highly sensitive and reliable capacitive pressure sensor for ultralow-pressure measurement by sandwiching MXene (Ti₃C₂T_x)/poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE) composite nanofibrous scaffolds as a dielectric layer between biocompatible poly-(3,4-ethylenedioxythiophene) polystyrene sulfonate /polydimethylsiloxane electrodes. The fabricated sensor exhibits a high sensitivity of 0.51 kPa^-1 and a minimum detection limit of 1.5 Pa. In addition, it also enables linear sensing over a broad pressure range (0-400 kPa) and high reliability over 10,000 cycles even at extremely high pressure (>167 kPa). The sensitivity of the nanofiber-based sensor is enhanced by MXene loading, thereby increasing the dielectric constant up to 40 and reducing the compression modulus to 58% compared with pristine PVDF-TrFE nanofiber scaffolds. The proposed sensor can be used to determine the health condition of patients by monitoring physiological signals (pulse rate, respiration, muscle movements, and eye twitching) and also represents a good candidate for a next generation human-machine interfacing device.

Flexible quality of service model for wireless body area sensor networks

Wireless body area sensor networks (WBASNs) are becoming an increasingly significant breakthrough technology for smart healthcare systems, enabling improved clinical decision-making in daily medical care. Recently, radio frequency ultra-wideband technology has developed substantially for physiological signal monitoring due to its advantages such as low-power consumption, high transmission data rate, and miniature antenna size. Applications of future ubiquitous healthcare systems offer the prospect of collecting human vital signs, early detection of abnormal medical conditions, real-time healthcare data transmission and remote telemedicine support. However, due to the technical constraints of sensor batteries, the supply of power is a major bottleneck for healthcare system design. Moreover, medium access control (MAC) needs to support reliable transmission links that allow sensors to transmit data safely and stably. In this Letter, the authors provide a flexible quality of service model for ad hoc networks that can support fast data transmission, adaptive schedule MAC control, and energy efficient ubiquitous WBASN networks. Results show that the proposed multi-hop communication ad hoc network model can balance information packet collisions and power consumption. Additionally, wireless communications link in WBASNs can effectively overcome multi-user interference and offer high transmission data rates for healthcare systems.

Physiological Signal Monitoring Bed for Infants Based on Load-Cell Sensors

Ballistocardiographs (BCGs), which record the mechanical activity of the heart, have been a subject of interest for several years because of their advantages in providing unobtrusive physiological measurements. BCGs could also be useful for monitoring the biological signals of infants without the need for physical confinement. In this study, we describe a physiological signal monitoring bed based on load cells and assess an algorithm to extract the heart rate and breathing rate from the measured load-cell signals. Four infants participated in a total of 13 experiments. As a reference signal, electrocardiogram and respiration signals were simultaneously measured using a commercial device. The proposed automatic algorithm then selected the optimal sensor from which to estimate the heartbeat and respiration information. The results from the load-cell sensor signals were compared with those of the reference signals, and the heartbeat and respiration information were found to have average performance errors of 2.55% and 2.66%, respectively. The experimental results verify the positive feasibility of BCG-based measurements in infants.