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HighlightsThis work reviews the development of breathable electronic skins (e-skins) for daily physiological signal monitoring in recent years.The design methods, preparation processes, and performances of typical breathable e-skin electrodes, breathable e-skin sensors, and breathable e-skin systems developed these years are systematically introduced and discussed.This review analyzes the challenges that breathable e-skins may face in daily health monitoring and puts forward the possible development directions of breathable e-skins.With the aging of society and the increase in people’s concern for personal health, long-term physiological signal monitoring in daily life is in demand. In recent years, electronic skin (e-skin) for daily health monitoring applications has achieved rapid development due to its advantages in high-quality physiological signals monitoring and suitability for system integrations. Among them, the breathable e-skin has developed rapidly in recent years because it adapts to the long-term and high-comfort wear requirements of monitoring physiological signals in daily life. In this review, the recent achievements of breathable e-skins for daily physiological monitoring are systematically introduced and discussed. By dividing them into breathable e-skin electrodes, breathable e-skin sensors, and breathable e-skin systems, we sort out their design ideas, manufacturing processes, performances, and applications and show their advantages in long-term physiological signal monitoring in daily life. In addition, the development directions and challenges of the breathable e-skin are discussed and prospected.

HighlightsThis article summarizes the recent advances in wireless, batteryless, implantable electronics for continuous physiological monitoring.The critical factors that affect the design of implantable electronics for biosensing are discussed.The recent progress of material research for developing various implantable devices is summarized.This article reviews various biomedical applications of implantable devices for human healthcare.This review summarizes recent progress in developing wireless, batteryless, fully implantable biomedical devices for real-time continuous physiological signal monitoring, focusing on advancing human health care. Design considerations, such as biological constraints, energy sourcing, and wireless communication, are discussed in achieving the desired performance of the devices and enhanced interface with human tissues. In addition, we review the recent achievements in materials used for developing implantable systems, emphasizing their importance in achieving multi-functionalities, biocompatibility, and hemocompatibility. The wireless, batteryless devices offer minimally invasive device insertion to the body, enabling portable health monitoring and advanced disease diagnosis. Lastly, we summarize the most recent practical applications of advanced implantable devices for human health care, highlighting their potential for immediate commercialization and clinical uses.

As the focus on physical health increases, the market demand for flexible wearable sensors increases. Textiles combined with sensitive materials and electronic circuits can form flexible, breathable high-performance sensors for physiological-signal monitoring. Carbon-based materials such as graphene, carbon nanotubes (CNTs), and carbon black (CB) have been widely utilized in the development of flexible wearable sensors due to their high electrical conductivity, low toxicity, low mass density, and easy functionalization. This review provides an overview of recent advancements in carbon-based flexible textile sensors, highlighting the development, properties, and applications of graphene, CNTs, and CB for flexible textile sensors. The physiological signals that can be monitored by carbon-based textile sensors include electrocardiogram (ECG), human body movement, pulse and respiration, body temperature, and tactile perception. We categorize and describe carbon-based textile sensors based on the physiological signals they monitor. Finally, we discuss the current challenges associated with carbon-based textile sensors and explore the future direction of textile sensors for monitoring physiological signals.

Carbon nanotubes (CNTs) and graphene have commonly been applied as the sensitive layer of strain sensors. However, the buckling deformation of CNTs and the crack generation of graphene usually leads to an unsatisfactory strain sensing performance. In this work, we developed a universal strategy to prepare welded CNT–graphene hybrids with tunable compositions and a tunable bonding strength between components by the in situ reduction of CNT–graphene oxide (GO) hybrid by thermal annealing. The stiffness of the hybrid film could be tailored by both initial CNT/GO dosage and annealing temperature, through which its electromechanical behaviors could also be defined. The strain sensor based on the CNT–graphene hybrid could be applied to collect epidermal bio-signals by both capturing the faint skin deformation from wrist pulse and recording the large deformations from joint bending, which has great potential in health monitoring, motion sensing and human–machine interfacing.

Condition monitoring (CM) signals play a critical role in assessing the remaining useful life of in-service components. In this article, an alternative view on modeling CM signals is proposed. This view draws its roots from multitask learning and is based on treating each CM signal as an individual task. Each task is then expressed as a convolution of a latent function drawn from a Gaussian process (GP), and the transfer of knowledge is achieved through sharing these latent functions between historical and in-service CM signals. Aside from being nonparametric, the flexible and individualistic approach in our model can account for heterogeneity in the data and automatically infer the commonalities between the new testing observations and CM signals in the historical dataset. The robustness and advantageous features of the proposed method are demonstrated through numerical studies and a case study with real-world data in the application to find the remaining useful life prediction of automotive lead-acid batteries. Technical details and additional numerical results are available in the supplementary materials.

Flexible pressure sensors capable of monitoring diverse physiological signals and body movements have garnered tremendous attention in wearable electronic devices. Thereinto, high constant sensitivity over a wide pressure range combined with breathability, biocompatibility, and biodegradability is pivotal for manufacturing of reliable pressure sensors in practical sensing applications. In this work, inspired by the multilayered structure of skin epidermis, we propose and demonstrate a multi-attribute wearable piezoresistive pressure sensor consisting of multilayered gradient conductive poly(ε-caprolactone) nanofiber membranes composites. In response to externally applied pressure, a layer-by-layer current path is activated inside the multilayered membranes composites, leading to the most salient sensing performance of high constant sensitivity of 33.955 kPa −1 within the pressure range of 0–80 kPa. The proposed pressure sensor also exhibits a fast response—relaxation time, a low detection limit, and excellent stability, which can be successfully used to measure human physiological signals. Lastly, an integrated sensor array system that can locate objects’ positions is constructed and applied to simulate sitting posture monitoring. These results indicate that the proposed pressure sensor holds great potential in health monitoring and wearable electronic devices.

Electrophysiological monitoring is a commonly used medical procedure designed to capture the electrical signals generated by the body and promptly identify any abnormal health conditions. Wearable sensors are of great significance in signal acquisition for electrophysiological monitoring. Traditional electrophysiological monitoring devices are often bulky and have many complex accessories and thus, are only suitable for limited application scenarios. Hydrogels optimized based on nanomaterials are lightweight with excellent stretchable and electrical properties, solving the problem of high-quality signal acquisition for wearable sensors. Therefore, the development of hydrogels based on nanomaterials brings tremendous potential for wearable physiological signal monitoring sensors. This review first introduces the latest advancement of hydrogels made from different nanomaterials, such as nanocarbon materials, nanometal materials, and two-dimensional transition metal compounds, in physiological signal monitoring sensors. Second, the versatile properties of these stretchable composite hydrogel sensors are reviewed. Then, their applications in various electrophysiological signal monitoring, such as electrocardiogram monitoring, electromyographic signal analysis, and electroencephalogram monitoring, are discussed. Finally, the current application status and future development prospects of nanomaterial-optimized hydrogels in wearable physiological signal monitoring sensors are summarized. We hope this review will inspire future development of wearable electrophysiological signal monitoring sensors using nanomaterial-based hydrogels.

Cerebral palsy is a disorder of central motor and postural development, resulting in limited mobility. Cerebral palsy is often accompanied by cognitive impairment and abnormal behavior, significantly impacting individuals and society. Time, energy, and economic investment in the rehabilitation process is substantial, yet the rehabilitation outcomes often remain unsatisfactory. Additionally, some patients have limited sensory perception during rehabilitation training, making it challenging to effectively regulate exercise intensity. Traditional evaluation methods are mostly based on recovery performance, lack guidance at the neurophysiological level, and have an unequal distribution of medical rehabilitation resources, which pose great challenges to the rehabilitation of patients. Based on the issues mentioned above, this paper proposes a real-time cerebral signal monitoring system based on wearable devices. This system can monitor and store blood oxygen, heart rate, myoelectric, and EEG signals during cerebral palsy rehabilitation, and it can track and monitor signals during the rehabilitation treatment process. The system includes two parts: hardware design and software design. The hardware design includes a data signal acquisition module, a main control chip (ESP32), a muscle electrical sensor module, a brain electrical sensor module, a blood/heart rate acquisition module, etc. It is primarily for real-time signal data acquisition, processing, and uploading to the cloud server. The software design includes functions such as data receiving, data processing, data storage, network configuration, and remote communication and enables the visual monitoring of data signals. The system can achieve real-time monitoring of electromyography, electroencephalography, and blood oxygen levels, as well as the heart rate of patients with cerebral palsy, and adjust rehabilitation training in real-time during the rehabilitation process. At the same time, based on the real-time storage of the original electromyography and electroencephalography data, it can provide auxiliary guidance for later rehabilitation evaluation and effective data support for the entire rehabilitation treatment process.

Pulsatile signal detection plays an important role in monitoring various physiological parameters, primarily heart rate and blood oxygen saturation. Their applications range from clinical settings to personal health and wellness monitoring. PPG (photoplethysmography) can provide non-invasive optical measurements to detect blood volume changes in peripheral tissues. Yet, it suffers from low spatial resolution to precisely detect the pulsatile signal originating over 2 mm in human tissue. Ultrasound (US) provides a deep detectable range compared to the pure optical method. However, its low contrast to red blood cells and cluster artifacts makes it only detect the indirect pulsation from the surrounding tissue of blood vessels. Recent advances in PA imaging show its capability to precisely measure pulsatile signals originating from blood vessels in deep regions (over 10 mm) and its potential to accurately record blood oxygen saturation with high spatial and temporal resolution. This review article summarizes studies on photoacoustic (PA) pulsatile signal monitoring, highlights the technical advances, and compares it against optical and ultrasonic approaches.

Spoofing interference poses a significant challenge to the Global Navigation Satellite System (GNSS). To effectively combat intermediate spoofing signals, this paper presents an enhanced spoofing detection method based on abnormal energy of the quadrature (Q) channel correlators. The detailed principle of this detection method is introduced based on the received signal model under spoofing attack. The normalization parameter used in this method was the estimation of the noise floor. The performance of the proposed Q energy detector is validated through simulations, the Texas Spoofing Test Battery dataset and field tests. The results demonstrate that the proposed detector significantly enhances detection performance compared to signal quality monitoring methods, particularly in overpowered scenarios and dynamic scenarios. By increasing the detection probability in the presence of spoofing signals and decreasing the false alarm probability in the absence of spoofing signals, the proposed detector can better meet the requirements of practical applications.