Bionic Ultra-Sensitive Self-Powered Electromechanical Sensor for Muscle-Triggered Communication Application

Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments

Human-machine voice interaction based on speech recognition offers an intuitive, efficient, and user-friendly interface, attracting wide attention in applications such as health monitoring, post-disaster rescue, and intelligent control. However, conventional microphone-based systems remain challenging for complex human-machine collaboration in noisy environments. Herein, an anti-noise triboelectric acoustic sensor (Anti-noise TEAS) based on flexible nanopillar structures is developed and integrated with a convolutional neural network-based deep learning model (Anti-noise TEAS-DLM). This highly synergistic system enables robust acoustic signal recognition for human-machine collaboration in complex, noisy scenarios. The Anti-noise TEAS directly captures acoustic fundamental frequency signals from laryngeal mixed-mode vibrations through contact sensing, while effectively suppressing environmental noise by optimizing device-structure buffering. The acoustic signals are subsequently processed and semantically decoded by the DLM, ensuring high-fidelity interpretation. Evaluated in both simulated virtual and real-life noisy environments, the Anti-noise TEAS-DLM demonstrates near-perfect noise immunity and reliably transmits various voice commands to guide robotic systems in executing complex post-disaster rescue tasks with high precision. The combined anti-noise robustness and execution accuracy endow this DLM-enhanced Anti-noise TEAS as a highly promising platform for next-generation human-machine collaborative systems operating in challenging noisy environments.

Machine learning-assisted wearable sensing systems for speech recognition and interaction

The human voice stands out for its rich information transmission capabilities. However, voice communication is susceptible to interference from noisy environments and obstacles. Here, we propose a wearable wireless flexible skin-attached acoustic sensor (SAAS) capable of capturing the vibrations of vocal organs and skin movements, thereby enabling voice recognition and human-machine interaction (HMI) in harsh acoustic environments. This system utilizes a piezoelectric micromachined ultrasonic transducers (PMUT), which feature high sensitivity (-198 dB), wide bandwidth (10 Hz-20 kHz), and excellent flatness (±0.5 dB). Flexible packaging enhances comfort and adaptability during wear, while integration with the Residual Network (ResNet) architecture significantly improves the classification of laryngeal speech features, achieving an accuracy exceeding 96%. Furthermore, we also demonstrated SAAS's data collection and intelligent classification capabilities in multiple HMI scenarios. Finally, the speech recognition system was able to recognize everyday sentences spoken by participants with an accuracy of 99.8% through a deep learning model. With advantages including a simple fabrication process, stable performance, easy integration, and low cost, SAAS presents a compelling solution for applications in voice control, HMI, and wearable electronics.

Recent Developments and Applications of Tactile Sensors with Biomimetic Microstructures

Humans possess an innate ability to perceive a wide range of objects through touch, which allows them to interact effectively with their surroundings. Similarly, tactile perception in artificial sensory systems enables the acquisition of object properties, human physiological signals, and environmental information. Biomimetic tactile sensors, as an emerging sensing technology, draw inspiration from biological systems and exhibit high sensitivity, rapid response, multimodal perception, and stability. By mimicking biological mechanisms and microstructures, these sensors achieve precise detection of mechanical signals, thereby paving the way for advancements in tactile sensing applications. This review provides an overview of key sensing mechanisms, microstructure designs, and advanced fabrication techniques of biomimetic tactile sensors. The system architecture design of biomimetic tactile sensing systems is also explored. Furthermore, the review highlights significant applications of these sensors in recent years, including texture recognition, human health detection, and human-machine interaction. Finally, the key challenges and future development prospects related to biomimetic tactile sensors are discussed.

Emerging Wearable Acoustic Sensing Technologies

Sound signals not only serve as the primary communication medium but also find application in fields such as medical diagnosis and fault detection. With public healthcare resources increasingly under pressure, and challenges faced by disabled individuals on a daily basis, solutions that facilitate low-cost private healthcare hold considerable promise. Acoustic methods have been widely studied because of their lower technical complexity compared to other medical solutions, as well as the high safety threshold of the human body to acoustic energy. Furthermore, with the recent development of artificial intelligence technology applied to speech recognition, speech recognition devices, and systems capable of assisting disabled individuals in interacting with scenes are constantly being updated. This review meticulously summarizes the sensing mechanisms, materials, structural design, and multidisciplinary applications of wearable acoustic devices applied to human health and human-computer interaction. Further, the advantages and disadvantages of the different approaches used in flexible acoustic devices in various fields are examined. Finally, the current challenges and a roadmap for future research are analyzed based on existing research progress to achieve more comprehensive and personalized healthcare.

Mechanically Robust Triboelectric Aerogels Enabled by Dense Bridging of MXene

Aerogels are widely applied for construction, aerospace, military, and energy owing to their lightweight, high specific surface area, and high porosity. The high porosity of aerogels often leads to a lack of mechanical strength, which limits their applications. Here, this study reports a mechanically robust MXene/cellulose nanocrystal composite aerogel enabled by inducing dense bridging through salting-out. First, MXene sheets are bridged with cellulose molecular chains via hydrogen bonds, and further dense bridging is constructed by promoting hydrogen bond formation through salting-out. By enhancing hydrogen bonding, the interlayer spacing of MXene sheets is reduced and their orientation is improved, effectively increasing the energy dissipation capacity of the porous structure. The aerogel exhibits a Young's modulus of 72.4 MPa, a specific modulus of 342.0 kN m/kg. An aerogel is used as a triboelectric material to construct a highly robust triboelectric nanogenerator. This study provides an effective strategy for the preparation of the mechanically robust aerogels.

Optimization of Chinese film and television communication technology in Portuguese-speaking areas under the application of digital Internet of Things

With the continuous change and progress of the world system, cultural communication has become a vital topic in today's society. This study aims to optimize the communication effect of Chinese film in Portuguese-speaking countries and promote the development of cultural communication in China through deep learning (DL) technology. First, this study utilizes DL technology to design film feature recognition and classification models to provide technical support for Chinese film communication. Second, by exploring the principle, development, and function of the digital Internet of Things, the study analyzes the evolution and spread of Chinese film in Portuguese-speaking countries and uses the Bayes algorithm to optimize the model. The results show that the calculation time of the designed model is shorter and the accuracy is higher, which offers an important reference for the effective communication of Chinese films in Portuguese-speaking countries. Hence, this study not only provides technical support for the improvement of the international communication effect of Chinese film but also contributes to the development of cultural communication in China.

Multifunctionally disordered TiO2 nanoneedles prevent periprosthetic infection and enhance osteointegration by killing bacteria and modulating the osteoimmune microenvironment

Rationale: Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are effective interventions for end-stage osteoarthritis; however, periprosthetic infection is a devastating complication of arthroplasty. To safely prevent periprosthetic infection and enhance osteointegration, the surface modification strategy was utilized to kill bacteria, modulate the osteoimmune microenvironment, and improve new bone formation. Methods: We used the hydrothermal method to fabricate a bionic insect wing with the disordered titanium dioxide nanoneedle (TNN) coating. The mussel-inspired poly-dopamine (PDA) and antibacterial silver nanoparticles (AgNPs) were coated on TNN, named AgNPs-PDA@TNN, to improve the biocompatibility and long-lasting bactericidal capacity. The physicochemical properties of the engineered specimen were evaluated with SEM, AFM, XPS spectrum, and water contact assay. The biocompatibility, bactericidal ability, and the effects on macrophages and osteogenic differentiation were assessed with RT-qPCR, Western blotting, live/dead staining, immunofluorescent staining, etc. Results: The AgNPs-PDA@TNN were biocompatible with macrophages and exhibited immunomodulatory ability to promote M2 macrophage polarization. In addition, AgNPs-PDA@TNN ameliorated the cytotoxicity caused by AgNPs, promoted cell spreading, and increased osteogenesis and matrix deposition of BMSCs. Furthermore, AgNPs-PDA@TNN exhibited bactericidal ability against E. coli and S. aureus by the bionic nanostructure and coated AgNPs. Various imaging analyses indicated the enhanced bactericidal ability and improved new bone formation by AgNPs-PDA@TNN in vivo. H&E, Gram, and Masson staining, verified the improved bone formation, less inflammation, infection, and fibrosis encapsulation. The immunofluorescence staining confirmed the immunomodulatory ability of AgNPs-PDA@TNN in vivo. Conclusion: The bionic insect wing AgNPs-PDA@TNN coating exhibited bactericidal property, immunomodulatory ability, and enhanced osteointegration. Thus, this multidimensional bionic implant surface holds promise as a novel strategy to prevent periprosthetic infection.

Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics

Owing to the advancement of interdisciplinary concepts, for example, wearable electronics, bioelectronics, and intelligent sensing, during the microelectronics industrial revolution, nowadays, extensively mature wearable sensing devices have become new favorites in the noninvasive human healthcare industry. The combination of wearable sensing devices with bionics is driving frontier developments in various fields, such as personalized medical monitoring and flexible electronics, due to the superior biocompatibilities and diverse sensing mechanisms. It is noticed that the integration of desired functions into wearable device materials can be realized by grafting biomimetic intelligence. Therefore, herein, the mechanism by which biomimetic materials satisfy and further enhance system functionality is reviewed. Next, wearable artificial sensory systems that integrate biomimetic sensing into portable sensing devices are introduced, which have received significant attention from the industry owing to their novel sensing approaches and portabilities. To address the limitations encountered by important signal and data units in biomimetic wearable sensing systems, two paths forward are identified and current challenges and opportunities are presented in this field. In summary, this review provides a further comprehensive understanding of the development of biomimetic wearable sensing devices from both breadth and depth perspectives, offering valuable guidance for future research and application expansion of these devices.

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid-solid TENG and liquid-solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.

Bioinspired nanoplatforms for human-machine interfaces: Recent progress in materials and device applications

The panoramic characteristics of human-machine interfaces (HMIs) have prompted the needs to update the biotechnology community with the recent trends, developments, and future research direction toward next-generation bioelectronics. Bioinspired materials are promising for integrating various bioelectronic devices to realize HMIs. With the advancement of scientific biotechnology, state-of-the-art bioelectronic applications have been extensively investigated to improve the quality of life by developing and integrating bioinspired nanoplatforms in HMIs. This review highlights recent trends and developments in the field of biotechnology based on bioinspired nanoplatforms by demonstrating recently explored materials and cutting-edge device applications. Section 1 introduces the recent trends and developments of bioinspired nanomaterials for HMIs. Section 2 reviews various flexible, wearable, biocompatible, and biodegradable nanoplatforms for bioinspired applications. Section 3 furnishes recently explored substrates as carriers for advanced nanomaterials in developing HMIs. Section 4 addresses recently invented biomimetic neuroelectronic, nanointerfaces, biointerfaces, and nano/microfluidic wearable bioelectronic devices for various HMI applications, such as healthcare, biopotential monitoring, and body fluid monitoring. Section 5 outlines designing and engineering of bioinspired sensors for HMIs. Finally, the challenges and opportunities for next-generation bioinspired nanoplatforms in extending the potential on HMIs are discussed for a near-future scenario. We believe this review can stimulate the integration of bioinspired nanoplatforms into the HMIs in addition to wearable electronic skin and health-monitoring devices while addressing prevailing and future healthcare and material-related problems in biotechnologies.

Personalized Machine Learning-Coupled Nanopillar Triboelectric Pulse Sensor for Cuffless Blood Pressure Continuous Monitoring

A wearable system that can continuously track the fluctuation of blood pressure (BP) based on pulse signals is highly desirable for the treatments of cardiovascular diseases, yet the sensitivity, reliability, and accuracy remain challenging. Since the correlations of pulse waveforms to BP are highly individualized due to the diversity of the patients' physiological characteristics, wearable sensors based on universal designs and algorithms often fail to derive BP accurately when applied on individual patients. Herein, a wearable triboelectric pulse sensor based on a biomimetic nanopillar layer was developed and coupled with Personalized Machine Learning (ML) to provide accurate and continuous monitoring of BP. Flexible conductive nanopillars as the triboelectric layer were fabricated through soft lithography replication of a cicada wing, which could effectively enhance the sensor's output performance to detect weak signal characteristics of pulse waveform for BP derivation. The sensors were coupled with a personalized Partial Least-Squares Regression (PLSR) ML to derive unknown BP based on individual pulse characteristics with reasonable accuracy, avoiding the issue of individual variability that was encountered by General PLSR ML or formula algorithms. The cuffless and intelligent design endow this ML-sensor as a highly promising platform for the care and treatments of hypertensive patients.

Flexible tactile sensors with biomimetic microstructures: Mechanisms, fabrication, and applications

In recent years, flexible devices have gained rapid development with great potential in daily life. As the core component of wearable devices, flexible tactile sensors are prized for their excellent properties such as lightweight, stretchable and foldable. Consequently, numerous high-performance sensors have been developed, along with an array of innovative fabrication processes. It has been recognized that the improvement of the single performance index for flexible tactile sensors is not enough for practical sensing applications. Therefore, balancing and optimization of overall performance of the sensor are extensively anticipated. Furthermore, new functional characteristics are required for practical applications, such as freeze resistance, corrosion resistance, self-cleaning, and degradability. From a bionic perspective, the overall performance of a sensor can be optimized by constructing bionic microstructures which can deliver additional functional features. This review briefly summarizes the latest developments in bionic microstructures for different types of tactile sensors and critically analyzes the sensing performance of fabricated flexible tactile sensors. Based on this, the application prospects of bionic microstructure-based tactile sensors in human detection and human-machine interaction devices are introduced.

Stretchable graded multichannel self-powered respiratory sensor inspired by shark gill

Respiratory sensing provides a simple, non-invasive, and efficient way for medical diagnosis and health monitoring, but it relies on sensors that are conformal, accurate, durable, and sustainable working. Here, a stretchable, multichannel respiratory sensor inspired by the structure of shark gill cleft is reported. The bionic shark gill structure can convert transverse elastic deformation into longitudinal elastic deformation during stretching. Combining the optimized bionic shark gill structure with the piezoelectric and the triboelectric effect, the bionic shark gill respiratory sensor (BSG-RS) can produce a graded electrical response to different tensile strains. Based on this feature, BSG-RS can simultaneously monitor the breathing rate and breathing depth of the human body accurately, and realize the effective recognition of the different human body's breathing state under the supporting software. With good stretchability, wearability, accuracy, and long-term stability (50,000 cycles), BSG-RS is expected to be applied as self-powered smart wearables for mobile medical diagnostic analysis in the future.

Bioinspired Stretchable Fiber-Based Sensor toward Intelligent Human-Machine Interactions

Wearable integrated sensing devices with flexible electronic elements exhibit enormous potential in human-machine interfaces (HMI), but they have limitations such as complex structures, poor waterproofness, and electromagnetic interference. Herein, inspired by the profile of Lindernia nummularifolia (LN), a bionic stretchable optical strain (BSOS) sensor composed of an LN-shaped optical fiber incorporated with a stretchable substrate is developed for intelligent HMI. Such a sensor enables large strain and bending angle measurements with temperature self-compensation by the intensity difference of two fiber Bragg gratings' (FBGs') center wavelength. Such configurations enable an excellent tensile strain range of up to 80%, moreover, leading to ultrasensitivity, durability (≥20,000 cycles), and waterproofness. The sensor is also capable of measuring different human activities and achieving HMI control, including immersive virtual reality, robot remote interactive control, and personal hands-free communication. Combined with the machine learning technique, gesture classification can be achieved using muscle activity signals captured from the BSOS sensor, which can be employed to obtain the motion intention of the prosthetic. These merits effectively indicate its potential as a solution for medical care HMI and show promise in smart medical and rehabilitation medicine.

A Self-Powered Triboelectric Hybrid Coder for Human-Machine Interaction

Human-machine interfaces have penetrated various academia and industry fields such as smartphones, robotic, virtual reality, and wearable electronics, due to their abundant functional sensors and information interaction methods. Nevertheless, most sensors' complex structural design, monotonous parameter detection capability, and single information coding communication hinder their rapid development. As the frontier of self-powered sensors, the triboelectric nanogenerator (TENG) has multiple working modes and high structural adaptability, which is a potential solution for multi-parameter sensing and miniaturizing of traditional interactive electronic devices. Herein, a self-powered hybrid coder (SHC) based on TENG is reported to encode two action parameters of touch and press, which can be used as a smart interface for human-machine interaction. The top-down hollow structure of the SHC, not only constructs a compositing mode to generate stable touch and press signals but also builds a hybrid coding platform for generating action codes in synergy mode. When a finger touches or presses the SHC, Morse code and Gray code can be transmitted for text information or remote control of electric devices. This self-powered coder is of reference value for designing an alternative human-machine interface and having the potential to contribute to the next generation of highly integrated portable smart electronics.

Wearable biosensors for real-time sweat analysis and body motion capture based on stretchable fiber-based triboelectric nanogenerators

Carbon neutrality is a global green energy revolution meaning that the carbon dioxide can make ends meet. However, with the mushroom of the fifth generation wireless systems (5G) and the Internet of Things (IoT), it is a great challenge for powering the ubiquitous distributed devices, because the battery production and high overhead maintenance may bring more carbon emissions. Here, we present wearable biosensors for real-time sweat analysis and body motion capture based on stretchable fiber-based triboelectric nanogenerators (F-TENG). The F-TENG is made of stretchable conductive fiber (Ecoflex coating with polyaniline (PANI)) and varnished wires. Based on the coupling effect of triboelectric effect and enzymatic reaction (surface-triboelectric coupling effect), the wearable biosensors can not only precisely sense the motion states, but also detect glucose, creatinine and lactate acid in sweat in real-time. Importantly, the wearable devices can self-drive without any external power source and the response against glucose, creatinine and lactate acid can be up to 103%, 125% and 38%, respectively. On this basis, applications in biosensing and wireless communication have been demonstrated. This work exhibits a prospective potential application of F-TENG in IoT for diverse use.

Bioinspired Triboelectric Nanosensors for Self-Powered Wearable Applications

The sustainable operation of wearable sensors plays an important role in continuous and longtime health monitoring. Conventional batteries, which are bulky and rigid, do not satisfy these requirements and, rather, cause additional economic burdens and environmental problems by regular replacement of power sources. This article provides a review on an alternative solution in the form of self-powered devices that can harvest energy from the surrounding environment to support the operation of the wearable sensor. The Review starts with an introduction of the self-powered triboelectric nanosensors (TENSs) and its two independent modules: the energy harvester and the sensing module. The Review continues with the TENS-related bioinspired designs for wearable applications, while providing a bird's-eye view of their characteristics and applications. The ongoing challenges and prospects for providing personal healthcare with self-powered TENS are presented and discussed.

Recent Advances in Self-Powered Piezoelectric and Triboelectric Sensors: From Material and Structure Design to Frontier Applications of Artificial Intelligence

The development of artificial intelligence and the Internet of things has motivated extensive research on self-powered flexible sensors. The conventional sensor must be powered by a battery device, while innovative self-powered sensors can provide power for the sensing device. Self-powered flexible sensors can have higher mobility, wider distribution, and even wireless operation, while solving the problem of the limited life of the battery so that it can be continuously operated and widely utilized. In recent years, the studies on piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs) have mainly concentrated on self-powered flexible sensors. Self-powered flexible sensors based on PENGs and TENGs have been reported as sensing devices in many application fields, such as human health monitoring, environmental monitoring, wearable devices, electronic skin, human–machine interfaces, robots, and intelligent transportation and cities. This review summarizes the development process of the sensor in terms of material design and structural optimization, as well as introduces its frontier applications in related fields. We also look forward to the development prospects and future of self-powered flexible sensors.