Ultra-compact and high-performance suspended aluminum scandium nitride Lamb wave humidity sensor with a graphene oxide layer.

The utilization of Microelectromechanical Systems (MEMS) technology holds great significance for developing compact and high-performance humidity sensors in human healthcare, and the Internet of Things. However, several drawbacks of the current MEMS humidity sensors limit their applications, including their long response time, low sensitivity, relatively large sensing area, and incompatibility with a complementary metal-oxide-semiconductor (CMOS) process. To address these problems, a suspended aluminum scandium nitride (AlScN) Lamb wave humidity sensor utilizing a graphene oxide (GO) layer is firstly designed and fabricated. The theoretical and experimental results both show that the AlScN Lamb wave humidity sensor exhibits high sensing performance. The mass loading sensitivity of the sensor is one order higher than that of the normal surface acoustic wave (SAW) humidity sensor based on an aluminum nitride (AlN) film; thus the AlScN Lamb wave humidity sensor achieves high sensitivity (∼41.2 ppm per % RH) with only an 80 nm-thick GO film. In particular, the as-prepared suspended AlScN Lamb wave sensors are able to respond to the wide relative humidity (0-80% RH) change in 2 s, and the device size is ultra-compact (260 µm × 72 µm). Moreover, the sensor has an excellent linear response in the 0-80% RH range, great repeatability and long-term stability. Therefore, this work brings opportunities for the development of ultra-compact and high-performance humidity sensors.

Triboelectric Nanogenerator-Enabled Digital Twins in Civil Engineering Infrastructure 4.0: A Comprehensive Review.

The emergence of digital twins has ushered in a new era in civil engineering with a focus on achieving sustainable energy supply, real-time sensing, and rapid warning systems. These key development goals mean the arrival of Civil Engineering 4.0.The advent of triboelectric nanogenerators (TENGs) demonstrates the feasibility of energy harvesting and self-powered sensing. This review aims to provide a comprehensive analysis of the fundamental elements comprising civil infrastructure, encompassing various structures such as buildings, pavements, rail tracks, bridges, tunnels, and ports. First, an elaboration is provided on smart engineering structures with digital twins. Following that, the paper examines the impact of using TENG-enabled strategies on smart civil infrastructure through the integration of materials and structures. The various infrastructures provided by TENGs have been analyzed to identify the key research interest. These areas encompass a wide range of civil infrastructure characteristics, including safety, efficiency, energy conservation, and other related themes. The challenges and future perspectives of TENG-enabled smart civil infrastructure are briefly discussed in the final section. In conclusion, it is conceivable that in the near future, there will be a proliferation of smart civil infrastructure accompanied by sustainable and comprehensive smart services.

All-organic transparent plant e-skin for noninvasive phenotyping.

Real-time in situ monitoring of plant physiology is essential for establishing a phenotyping platform for precision agriculture. A key enabler for this monitoring is a device that can be noninvasively attached to plants and transduce their physiological status into digital data. Here, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate. This plant e-skin is optically and mechanically invisible to plants with no observable adverse effects to plant health. We demonstrate the capabilities of our plant e-skins as strain and temperature sensors, with the application to Brassica rapa leaves for collecting corresponding parameters under normal and abiotic stress conditions. Strains imposed on the leaf surface during growth as well as diurnal fluctuation of surface temperature were captured. We further present a digital-twin interface to visualize real-time plant surface environment, providing an intuitive and vivid platform for plant phenotyping.

Fiber-Based Noncontact Sensor with Stretchability for Underwater Wearable Sensing and VR Applications.

The rapid development of artificial intelligent wearable devices has led to an increasing need for seamless information exchange between humans, machines, and virtual spaces, often relying on touch sensors as the primary interaction medium. Additionally, the demand for underwater detection technologies is on the rise owing to the prevalent wet and submerged environment. Here, a fiber-based capacitive sensor with superior stretchability and hydrophobicity is proposed, designed to cater to noncontact and underwater applications. The sensor is constructed using bacterial cellulose (BC)@BC/carbon nanotubes (CNTs) (BBT) helical fiber as the matrix and methyltrimethoxysilane (MTMS) as the hydrophobic modified agent, forming a hydrophobic silylated BC@BC/CNT (SBBT) helical fiber by the chemical vapor deposition (CVD) technique. These fibers exhibit an impressive contact angle of 132.8°. The SBBT helicalfiber-based capacitive sensor presents capabilities for both noncontact and underwater sensing, which exhibits a significant capacitance change of -0.27 (at a distance of 0.5 cm). We have achieved interactive control between real space and virtual space through intelligent data analysis technology with minimal interference from the presence of water. This work has laid a solid foundation of noncontact sensing with attributes such as degradability, stretchability, and hydrophobicity. Moreover, it offers promising solutions for barrier-free communication in virtual reality (VR) and underwater applications, providing avenues for smart human-machine interfaces for submerged use.

Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform.

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

A Wearable Multidimensional Motion Sensor for AI-Enhanced VR Sports.

Regular exercise paves the way to a healthy life. However, conventional sports events are susceptible to weather conditions. Current motion sensors for home-based sports are mainly limited by operation power consumption, single-direction sensitivity, or inferior data analysis. Herein, by leveraging the 3-dimensional printing technique and triboelectric effect, a wearable self-powered multidimensional motion sensor has been developed to detect both the vertical and planar movement trajectory. By integrating with a belt, this sensor could be used to identify some low degree of freedom motions, e.g., waist or gait motion, with a high accuracy of 93.8%. Furthermore, when wearing the sensor at the ankle position, signals generated from shank motions that contain more abundant information could also be effectively collected. By means of a deep learning algorithm, the kicking direction and force could be precisely differentiated with an accuracy of 97.5%. Toward practical application, a virtual reality-enabled fitness game and a shooting game were successfully demonstrated. This work is believed to open up new insights for the development of future household sports or rehabilitation.

Human Machine Interface with Wearable Electronics Using Biodegradable Triboelectric Films for Calligraphy Practice and Correction.

Letter handwriting, especially stroke correction, is of great importance for recording languages and expressing and exchanging ideas for individual behavior and the public. In this study, a biodegradable and conductive carboxymethyl chitosan-silk fibroin (CSF) film is prepared to design wearable triboelectric nanogenerator (denoted as CSF-TENG), which outputs of Voc ≈ 165 V, Isc ≈ 1.4 µA, and Qsc ≈ 72 mW cm-2. Further, in vitro biodegradation of CSF film is performed through trypsin and lysozyme. The results show that trypsin and lysozyme have stable and favorable biodegradation properties, removing 63.1% of CSF film after degrading for 11 days. Further, the CSF-TENG-based human-machine interface (HMI) is designed to promptly track writing steps and access the accuracy of letters, resulting in a straightforward communication media of human and machine. The CSF-TENG-based HMI can automatically recognize and correct three representative letters (F, H, and K), which is benefited by HMI system for data processing and analysis. The CSF-TENG-based HMI can make decisions for the next stroke, highlighting the stroke in advance by replacing it with red, which can be a candidate for calligraphy practice and correction. Finally, various demonstrations are done in real-time to achieve virtual and real-world controls including writing, vehicle movements, and healthcare.

Flexible Forearm Triboelectric Sensors for Parkinson's Disease Diagnosing and Monitoring.

Existing approaches that assess and monitor the severity of Parkinson's Disease (PD) focus on the integration of wearable devices based on inertial sensors (accelerometers, gyroscopes) and electromyographic (EMG) transducers. Nevertheless, some of these sensors are bulky and lack comfortability. This manuscript presents triboelectric nanogenerators (TENGs) as an alternative stretchable sensor solution enabling PD monitoring systems. The prototype has been developed using a triboelectric sensor based on Ecoflex™ and PEDOT:PSS that is placed on the forearm. The movement of the skin above the forearm muscles and tendons correlates with the extension and flexion of fingers and hands. This way, the small gap of 0.5 cm between the polymer layers is displaced, generating voltage due to the triboelectric contact. Signals from preliminary experiments can discriminate different dynamics of emulated tremor and bradykinesia in hands and fingers. A modified version of the TS is integrated with a printed circuit board (PCB) in a single package with signal conditioning and wireless data transmission. The sensor platforms have demonstrated a good sensitivity to PD symptoms like bradykinesia and tremor based on the Unified Parkinson's Disease Rating Scale (MDS:UPDRS).

Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications.

Gait and waist motions always contain massive personnel information and it is feasible to extract these data via wearable electronics for identification and healthcare based on the Internet of Things (IoT). There also remains a demand to develop a cost-effective human-machine interface to enhance the immersion during the long-term rehabilitation. Meanwhile, triboelectric nanogenerator (TENG) revealing its merits in both wearable electronics and IoT tends to be a possible solution. Herein, the authors present wearable TENG-based devices for gait analysis and waist motion capture to enhance the intelligence and performance of the lower-limb and waist rehabilitation. Four triboelectric sensors are equidistantly sewed onto a fabric belt to recognize the waist motion, enabling the real-time robotic manipulation and virtual game for immersion-enhanced waist training. The insole equipped with two TENG sensors is designed for walking status detection and a 98.4% identification accuracy for five different humans aiming at rehabilitation plan selection is achieved by leveraging machine learning technology to further analyze the signals. Through a lower-limb rehabilitation robot, the authors demonstrate that the sensory system performs well in user recognition, motion monitoring, as well as robot and gaming-aided training, showing its potential in IoT-based smart healthcare applications.

AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove.

Sign language recognition, especially the sentence recognition, is of great significance for lowering the communication barrier between the hearing/speech impaired and the non-signers. The general glove solutions, which are employed to detect motions of our dexterous hands, only achieve recognizing discrete single gestures (i.e., numbers, letters, or words) instead of sentences, far from satisfying the meet of the signers' daily communication. Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface. Non-segmentation and segmentation assisted deep learning model achieves the recognition of 50 words and 20 sentences. Significantly, the segmentation approach splits entire sentence signals into word units. Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences. Furthermore, new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%. Finally, the sign language recognition results are projected into virtual space and translated into text and audio, allowing the remote and bidirectional communication between signers and non-signers.

A Motion Capturing and Energy Harvesting Hybridized Lower-Limb System for Rehabilitation and Sports Applications.

Lower-limb motion monitoring is highly desired in various application scenarios ranging from rehabilitation to sports training. However, there still lacks a cost-effective, energy-saving, and computational complexity-reducing solution for this specific demand. Here, a motion capturing and energy harvesting hybridized lower-limb (MC-EH-HL) system with 3D printing is demonstrated. It enables low-frequency biomechanical energy harvesting with a sliding block-rail piezoelectric generator (S-PEG) and lower-limb motion sensing with a ratchet-based triboelectric nanogenerator (R-TENG). A unique S-PEG is proposed with particularly designed mechanical structures to convert lower-limb 3D motion into 1D linear sliding on the rail. On the one hand, high output power is achieved with the S-PEG working at a very low frequency, which realizes self-sustainable systems for wireless sensing under the Internet of Things framework. On the other hand, the R-TENG gives rise to digitalized triboelectric output, matching the rotation angles to the pulse numbers. Additional physical parameters can be estimated to enrich the sensory dimension. Accordingly, demonstrative rehabilitation, human-machine interfacing in virtual reality, and sports monitoring are presented. This developed hybridized system exhibits an economic and energy-efficient solution to support the need for lower-limb motion tracking in various scenarios, paving the way for self-sustainable multidimensional motion tracking systems in near future.

Artificial Intelligence-Enabled Caregiving Walking Stick Powered by Ultra-Low-Frequency Human Motion.

The increasing population of the elderly and motion-impaired people brings a huge challenge to our social system. However, the walking stick as their essential tool has rarely been investigated into its potential capabilities beyond basic physical support, such as activity monitoring, tracing, and accident alert. Here, we report a walking stick powered by ultra-low-frequency human motion and equipped with deep-learning-enabled advanced sensing features to provide a healthcare-monitoring platform for motion-impaired users. A linear-to-rotary structure is designed to achieve highly efficient energy harvesting from the linear motion of a walking stick with ultralow frequency. Besides, two kinds of self-powered triboelectric sensors are proposed and integrated to extract the motion features of the walking stick. Augmented sensing functionalities with high accuracies have been enabled by deep-learning-based data analysis, including identity recognition, disability evaluation, and motion status distinguishing. Furthermore, a self-sustainable Internet of Things (IoT) system with global positioning system tracing and environmental temperature and humidity amenity sensing functions is obtained. Combined with the aforementioned functionalities, this walking stick is demonstrated in various usage scenarios as a caregiver for real-time well-being status and activity monitoring. The caregiving walking stick shows the potential of being an intelligent aid for motion-impaired users to help them live life with adequate autonomy and safety.

Flourishing energy harvesters for future body sensor network: from single to multiple energy sources.

Body sensor network (bodyNET) offers possibilities for future disease diagnosis, preventive health care, rehabilitation, and treatment. However, the eventual realization demands reliable and sustainable power sources. The flourishing energy harvesters (EHs) have provided prominent techniques for practically addressing the concurrent energy issue. Targeting for a specific energy source, wearable EHs with a sole conversion mechanism are well investigated. Hybrid EHs integrating different effects for a single source or multi-sources are attaining growing attention, for they provide another degree of freedom concerning a higher-level energy utility. Merging EHs with other functional electronics, diversified functional self-sustainable systems are developed, paving the way for the accomplishment of bodyNET. This review introduces the evolution of wearable EHs from a single effect to hybridized mechanisms for multiple energy sources and wearable to implantable self-sustainable systems. Last, we provide our perspectives on the future development of hybrid EHs to be more competitive with conventional batteries.

Deep learning enabled smart mats as a scalable floor monitoring system.

Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique "identity" electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security.

Wearable Triboelectric/Aluminum Nitride Nano-Energy-Nano-System with Self-Sustainable Photonic Modulation and Continuous Force Sensing.

Wearable photonics offer a promising platform to complement the thriving complex wearable electronics system by providing high-speed data transmission channels and robust optical sensing paths. Regarding the realization of photonic computation and tunable (de)multiplexing functions based on system-level integration of abundant photonic modulators, it is challenging to reduce the overwhelming power consumption in traditional current-based silicon photonic modulators. This issue is addressed by integrating voltage-based aluminum nitride (AlN) modulator and textile triboelectric nanogenerator (T-TENG) on a wearable platform to form a nano-energy-nano-system (NENS). The T-TENG transduces the mechanical stimulations into electrical signals based on the coupling of triboelectrification and electrostatic induction. The self-generated high-voltage from the T-TENG is applied to the AlN modulator and boosts its modulation efficiency regardless of AlN's moderate Pockels effect. Complementarily, the AlN modulator's capacitive nature enables the open-circuit operation mode of T-TENG, providing the integrated NENS with continuous force sensing capability which is notably uninfluenced by operation speeds. Furthermore, a physical model is proposed to describe the coupled AlN modulator/T-TENG system. With the enhanced photonic modulation and the open-circuit operation mode enabled by synergies between the AlN modulator and the T-TENG, optical Morse code transmission and continuous human motion monitoring are demonstrated for practical wearable applications.

Unveiling Stimulation Secrets of Electrical Excitation of Neural Tissue Using a Circuit Probability Theory.

Electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new theory, named the Circuit-Probability theory, to reveal how this physical interaction happen. The relation between the electrical stimulation input and the neural response can be theoretically calculated. We show that many empirical models, including strength-duration relationship and linear-non-linear-Poisson model, can be theoretically explained, derived, and amended using our theory. Furthermore, this theory can explain the complex non-linear and resonant phenomena and fit in vivo experiment data. In this letter, we validated an entirely new framework to study electrical stimulation on neural tissue, which is to simulate voltage waveforms using a parallel RLC circuit first, and then calculate the excitation probability stochastically.

Machine Learning Glove Using Self-Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications.

The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as an intuitive and low-cost HMI can expediently track the motions of human fingers, resulting in a straightforward communication media of human-machine interactions. When combining several triboelectric textile sensors and proper machine learning technique, it has great potential to realize complex gesture recognition with the minimalist-designed glove for the comprehensive control in both real and virtual space. However, humidity or sweat may negatively affect the triboelectric output as well as the textile itself. Hence, in this work, a facile carbon nanotubes/thermoplastic elastomer (CNTs/TPE) coating approach is investigated in detail to achieve superhydrophobicity of the triboelectric textile for performance improvement. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low-cost and self-powered interface for gesture recognition. By leveraging machine learning technology, various gesture recognition tasks are done in real time by using gestures to achieve highly accurate virtual reality/augmented reality (VR/AR) controls including gun shooting, baseball pitching, and flower arrangement, with minimized effect from sweat during operation.

Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications.

Human-machine interfaces (HMIs) experience increasing requirements for intuitive and effective manipulation. Current commercialized solutions of glove-based HMI are limited by either detectable motions or the huge cost on fabrication, energy, and computing power. We propose the haptic-feedback smart glove with triboelectric-based finger bending sensors, palm sliding sensor, and piezoelectric mechanical stimulators. The detection of multidirectional bending and sliding events is demonstrated in virtual space using the self-generated triboelectric signals for various degrees of freedom on human hand. We also perform haptic mechanical stimulation via piezoelectric chips to realize the augmented HMI. The smart glove achieves object recognition using machine learning technique, with an accuracy of 96%. Through the integrated demonstration of multidimensional manipulation, haptic feedback, and AI-based object recognition, our glove reveals its potential as a promising solution for low-cost and advanced human-machine interaction, which can benefit diversified areas, including entertainment, home healthcare, sports training, and medical industry.

Self-Sustainable Wearable Textile Nano-Energy Nano-System (NENS) for Next-Generation Healthcare Applications.

Wearable electronics presage a future in which healthcare monitoring and rehabilitation are enabled beyond the limitation of hospitals, and self-powered sensors and energy generators are key prerequisites for a self-sustainable wearable system. A triboelectric nanogenerator (TENG) based on textiles can be an optimal option for scavenging low-frequency and irregular waste energy from body motions as a power source for self-sustainable systems. However, the low output of most textile-based TENGs (T-TENGs) has hindered its way toward practical applications. In this work, a facile and universal strategy to enhance the triboelectric output is proposed by integration of a narrow-gap TENG textile with a high-voltage diode and a textile-based switch. The closed-loop current of the diode-enhanced textile-based TENG (D-T-TENG) can be increased by 25 times. The soft, flexible, and thin characteristics of the D-T-TENG enable a moderate output even as it is randomly scrunched. Furthermore, the enhanced current can directly stimulate rat muscle and nerve. In addition, the capability of the D-T-TENG as a practical power source for wearable sensors is demonstrated by powering Bluetooth sensors embedded to clothes for humidity and temperature sensing. Looking forward, the D-T-TENG renders an effective approach toward a self-sustainable wearable textile nano-energy nano-system for next-generation healthcare applications.