Soft Robotic Perception System with Ultrasonic Auto-Positioning and Multimodal Sensory Intelligence

Quantitative softness and texture bimodal haptic sensors for robotic clinical feature identification and intelligent picking

Replicating human somatosensory networks in robots is crucial for dexterous manipulation, ensuring the appropriate grasping force for objects of varying softness and textures. Despite advances in artificial haptic sensing for object recognition, accurately quantifying haptic perceptions to discern softness and texture remains challenging. Here, we report a methodology that uses a bimodal haptic sensor to capture multidimensional static and dynamic stimuli, allowing for the simultaneous quantification of softness and texture features. This method demonstrates synergistic measurements of elastic and frictional coefficients, thereby providing a universal strategy for acquiring the adaptive gripping force necessary for scarless, antislippage interaction with delicate objects. Equipped with this sensor, a robotic manipulator identifies porcine mucosal features with 98.44% accuracy and stably grasps visually indistinguishable mature white strawberries, enabling reliable tissue palpation and intelligent picking. The design concept and comprehensive guidelines presented would provide insights into haptic sensor development, promising benefits for robotics.

Bionic e-skin with precise multi-directional droplet sliding sensing for enhanced robotic perception

Electronic skins with deep and comprehensive liquid information detection are desired to endow intelligent robotic devices with augmented perception and autonomous regulation in common droplet environments. At present, one technical limitation of electronic skins is the inability to perceive the liquid sliding information as realistically as humans and give feedback in time. To this critical challenge, in this work, a self-powered bionic droplet electronic skin is proposed by constructing an ingenious co-layer interlaced electrode network and using an overpass connection method. The bionic skin is used for droplet environment reconnaissance and converts various dynamic droplet sliding behaviors into electrical signals based on triboelectricity. More importantly, the two-dimensional sliding behavior of liquid droplets is comprehensively perceived by the e-skin and visually fed back in real-time on an indicator. Furthermore, the flow direction warning and intelligent closed-loop control of water leakage are also achieved by this e-skin, achieving the effect of human neuromodulation. This strategy compensates for the limitations of e-skin sensing droplets and greatly narrows the gap between artificial e-skins and human skins in perceiving functions.

Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis

Electronic stethoscope used to detect cardiac sounds that contain essential clinical information is a primary tool for diagnosis of various cardiac disorders. However, the linear electromechanical constitutive relation makes conventional piezoelectric sensors rather ineffective to detect low-intensity, low-frequency heart acoustic signal without the assistance of complex filtering and amplification circuits. Herein, it is found that triboelectric sensor features superior advantages over piezoelectric one for microquantity sensing originated from the fast saturated constitutive characteristic. As a result, the triboelectric sensor shows ultrahigh sensitivity (1215 mV Pa-1) than the piezoelectric counterpart (21 mV Pa-1) in the sound pressure range of 50-80 dB under the same testing condition. By designing a trumpet-shaped auscultatory cavity with a power function cross-section to achieve acoustic energy converging and impedance matching, triboelectric stethoscope delivers 36 dB signal-to-noise ratio for human test (2.3 times of that for piezoelectric one). Further combining with machine learning, five cardiac states can be diagnosed at 97% accuracy. In general, the triboelectric sensor is distinctly unique in basic mechanism, provides a novel design concept for sensing micromechanical quantities, and presents significant potential for application in cardiac sounds sensing and disease diagnosis.

Phase-Directed Assembly of Triboelectric Nanopaper for Self-Powered Noncontact Sensing

Noncontact sensing technology serves as a pivotal medium for seamless data acquisition and intelligent perception in the era of the Internet of Things (IoT), bringing innovative interactive experiences to wearable human-machine interaction perception networks. However, the pervasive limitations of current noncontact sensing devices posed by harsh environmental conditions hinder the precision and stability of signals. In this study, the triboelectric nanopaper prepared by a phase-directed assembly strategy is presented, which possesses low charge transfer mobility (1618 cm2 V-1 s-1) and exceptional high-temperature stability. Wearable self-powered noncontact sensors constructed from triboelectric nanopaper operate stably under high temperatures (200 °C). Furthermore, a temperature warning system for workers in hazardous environments is demonstrated, capable of nonintrusively identifying harmful thermal stimuli and detecting motion status. This research not only establishes a technological foundation for accurate and stable noncontact sensing under high temperatures but also promotes the sustainable intelligent development of wearable IoT devices under extreme environments.

Advanced Ultrasound Energy Transfer Technologies using Metamaterial Structures

Wireless energy transfer (WET) based on ultrasound-driven generators with enormous beneficial functions, is technologically in progress by the valuation of ultrasonic metamaterials (UMMs) in science and engineering domains. Indeed, novel metamaterial structures can develop the efficiency of mechanical and physical features of ultrasound energy receivers (US-ETs), including ultrasound-driven piezoelectric and triboelectric nanogenerators (US-PENGs and US-TENGs) for advantageous applications. This review article first summarizes the fundamentals, classification, and design engineering of UMMs after introducing ultrasound energy for WET technology. In addition to addressing using UMMs, the topical progress of innovative UMMs in US-ETs is conceptually presented. Moreover, the advanced approaches of metamaterials are reported in the categorized applications of US-PENGs and US-TENGs. Finally, some current perspectives and encounters of UMMs in US-ETs are offered. With this objective in mind, this review explores the potential revolution of reliable integrated energy transfer systems through the transformation of metamaterials into ultrasound-driven active mediums for generators.

Self-Perceptional Soft Robotics by a Dielectric Elastomer

Soft robotics has been a rapidly growing field in recent decades due to its advantages of softness, deformability, and adaptability to various environments. However, the separation of perception and actuation in soft robot research hinders its progress toward compactness and flexibility. To address this limitation, we propose the use of a dielectric elastomer actuator (DEA), which exhibits both an actuation capability and perception stability. Specifically, we developed a DEA array to localize the 3D spatial position of objects. Subsequently, we integrate the actuation and sensing properties of DEA into soft robots to achieve self-perception. We have developed a system that integrates actuation and sensing and have proposed two modes to achieve this integration. Furthermore, we demonstrated the feasibility of this system for soft robots. When the robots detect an obstacle or an approaching object, they can swiftly respond by avoiding or escaping the obstacle. By eliminating the need for separate perception and motion considerations, self-perceptional soft robots can achieve an enhanced response performance and enable applications in a more compact and flexible field.

Integrated Actuation and Sensing: Toward Intelligent Soft Robots

Soft robotics has received substantial attention due to its remarkable deformability, making it well-suited for a wide range of applications in complex environments, such as medicine, rescue operations, and exploration. Within this domain, the interaction of actuation and sensing is of utmost importance for controlling the movements and functions of soft robots. Nonetheless, current research predominantly focuses on isolated actuation and sensing capabilities, often neglecting the critical integration of these 2 domains to achieve intelligent functionality. In this review, we present a comprehensive survey of fundamental actuation strategies and multimodal actuation while also delving into advancements in proprioceptive and haptic sensing and their fusion. We emphasize the importance of integrating actuation and sensing in soft robotics, presenting 3 integration methodologies, namely, sensor surface integration, sensor internal integration, and closed-loop system integration based on sensor feedback. Furthermore, we highlight the challenges in the field and suggest compelling directions for future research. Through this comprehensive synthesis, we aim to stimulate further curiosity among researchers and contribute to the development of genuinely intelligent soft robots.

Tactile corpuscle-inspired piezoresistive sensors based on (3-aminopropyl) triethoxysilane-enhanced CNPs/carboxylated MWCNTs/cellulosic fiber composites for textile electronics

Recently, wearable electronic products and gadgets have developed quickly with the aim of catching up to or perhaps surpassing the ability of human skin to perceive information from the external world, such as pressure and strain. In this study, by first treating the cellulosic fiber (modal textile) substrate with (3-aminopropyl) triethoxysilane (APTES) and then covering it with conductive nanocomposites, a bionic corpuscle layer is produced. The sandwich structure of tactile corpuscle-inspired bionic (TCB) piezoresistive sensors created with the layer-by-layer (LBL) technology consists of a pressure-sensitive module (a bionic corpuscle), interdigital electrodes (a bionic sensory nerve), and a PU membrane (a bionic epidermis). The synergistic mechanism of hydrogen bond and coupling agent helps to improve the adhesive properties of conductive materials, and thus improve the pressure sensitive properties. The TCB sensor possesses favorable sensitivity (1.0005 kPa-1), a wide linear sensing range (1700 kPa), and a rapid response time (40 ms). The sensor is expected to be applied in a wide range of possible applications including human movement tracking, wearable detection system, and textile electronics.

Multimodal Sensors Enabled Autonomous Soft Robotic System with Self-Adaptive Manipulation

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

Environmentally Robust Triboelectric Tire Monitoring System for Self-Powered Driving Information Recognition via Hybrid Deep Learning in Time-Frequency Representation

Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire-road friction. The optimization of the process and structure of a laser-induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200-2000 rpm and contact fractions of line. Employing a hybrid model combining short-term Fourier transform with a convolution neural network-long short-term memory, the LIG-based triboelectric tire monitoring (LTTM) system decouples the driving information, such as traffic lines and road states, from varied environmental conditions of humidity (10%-90%) and temperatures (50-70 °C). The real-time line and road state recognition of the LTTM system is confirmed on a mobile platform across diverse environmental conditions, including fog, dampness, intense sunlight, and heat shimmer. This work provides an environmentally robust monitoring AIoT system by introducing a self-powered triboelectric sensor and hybrid deep learning for smart mobility.

Self-healing and adhesive MXene-polypyrrole/silk fibroin/polyvinyl alcohol conductive hydrogels as wearable sensor

Conductive hydrogels become increasing attractive for flexible electronic devices and biosensors. However, challenges still remain in fabrication of flexible hydrogels with high electrical conductivity, self-healing capability and adhesion property. Herein, a conductive hydrogel (PSDM) was prepared by solution-gel method using MXene and dopamine modified polypyrrole as conductive enhanced materials, polyvinyl alcohol and silk fibroin as gel networks, and borax as cross-linking agent. Notably, the PSDM hydrogels not only showed high permeability (13.82 mg∙cm-2∙h-1), excellent stretch ability (1235 %), high electrical conductivity (11.3 S/m) and long-term stability, but also exhibited high adhesion performance and self-healing properties. PSDM hydrogels displayed outstanding sensing performance and durability for monitoring human activities including writing, finger bending and wrist bending. The PSDM hydrogel was made into wearable flexible electrodes and realized accurate, sensitive and reliable detection of human electromyographic and electrocardiographic signals. The sensor was also applied in human-computer interaction by collecting electromyography signals of different gestures for machine learning and gesture recognition. According to 480 groups of data collected, the recognition accuracy of gestures by the electrodes was close to 100 %, indicating that the PSDM hydrogel electrodes possessed excellent sensing performance for high precision data acquisition and human-computer interaction interface.

AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

An Emerging Era: Conformable Ultrasound Electronics

Conformable electronics are regarded as the next generation of personal healthcare monitoring and remote diagnosis devices. In recent years, piezoelectric-based conformable ultrasound electronics (cUSE) have been intensively studied due to their unique capabilities, including nonradiative monitoring, soft tissue imaging, deep signal decoding, wireless power transfer, portability, and compatibility. This review provides a comprehensive understanding of cUSE for use in biomedical and healthcare monitoring systems and a summary of their recent advancements. Following an introduction to the fundamentals of piezoelectrics and ultrasound transducers, the critical parameters for transducer design are discussed. Next, five types of cUSE with their advantages and limitations are highlighted, and the fabrication of cUSE using advanced technologies is discussed. In addition, the working function, acoustic performance, and accomplishments in various applications are thoroughly summarized. It is noted that application considerations must be given to the tradeoffs between material selection, manufacturing processes, acoustic performance, mechanical integrity, and the entire integrated system. Finally, current challenges and directions for the development of cUSE are highlighted, and research flow is provided as the roadmap for future research. In conclusion, these advances in the fields of piezoelectric materials, ultrasound transducers, and conformable electronics spark an emerging era of biomedicine and personal healthcare.

A robotic sensory system with high spatiotemporal resolution for texture recognition

Humans can gently slide a finger on the surface of an object and identify it by capturing both static pressure and high-frequency vibrations. Although modern robots integrated with flexible sensors can precisely detect pressure, shear force, and strain, they still perform insufficiently or require multi-sensors to respond to both static and high-frequency physical stimuli during the interaction. Here, we report a real-time artificial sensory system for high-accuracy texture recognition based on a single iontronic slip-sensor, and propose a criterion-spatiotemporal resolution, to corelate the sensing performance with recognition capability. The sensor can respond to both static and dynamic stimuli (0-400 Hz) with a high spatial resolution of 15 μm in spacing and 6 μm in height, together with a high-frequency resolution of 0.02 Hz at 400 Hz, enabling high-precision discrimination of fine surface features. The sensory system integrated on a prosthetic fingertip can identify 20 different commercial textiles with a 100.0% accuracy at a fixed sliding rate and a 98.9% accuracy at random sliding rates. The sensory system is expected to help achieve subtle tactile sensation for robotics and prosthetics, and further be applied to haptic-based virtual reality and beyond.

Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

A Broad Range Triboelectric Stiffness Sensor for Variable Inclusions Recognition

With the development of artificial intelligence, stiffness sensors are extensively utilized in various fields, and their integration with robots for automated palpation has gained significant attention. This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator (Stiff-TENG) for variable inclusions in soft objects detection. The Stiff-TENG employs a stacked structure comprising an indium tin oxide film, an elastic sponge, a fluorinated ethylene propylene film with a conductive ink electrode, and two acrylic pieces with a shielding layer. Through the decoupling method, the Stiff-TENG achieves stiffness detection of objects within 1.0 s. The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed. The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures, enabling effective recognition of variable inclusions in soft object, reaching a recognition accuracy of 99.7%. Furthermore, its adaptability makes it well-suited for the detection of pathological conditions within the human body, as pathological tissues often exhibit changes in the stiffness of internal organs. This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.

Remote sensing traffic scene retrieval based on learning control algorithm for robot multimodal sensing information fusion and human-machine interaction and collaboration

In light of advancing socio-economic development and urban infrastructure, urban traffic congestion and accidents have become pressing issues. High-resolution remote sensing images are crucial for supporting urban geographic information systems (GIS), road planning, and vehicle navigation. Additionally, the emergence of robotics presents new possibilities for traffic management and road safety. This study introduces an innovative approach that combines attention mechanisms and robotic multimodal information fusion for retrieving traffic scenes from remote sensing images. Attention mechanisms focus on specific road and traffic features, reducing computation and enhancing detail capture. Graph neural algorithms improve scene retrieval accuracy. To achieve efficient traffic scene retrieval, a robot equipped with advanced sensing technology autonomously navigates urban environments, capturing high-accuracy, wide-coverage images. This facilitates comprehensive traffic databases and real-time traffic information retrieval for precise traffic management. Extensive experiments on large-scale remote sensing datasets demonstrate the feasibility and effectiveness of this approach. The integration of attention mechanisms, graph neural algorithms, and robotic multimodal information fusion enhances traffic scene retrieval, promising improved information extraction accuracy for more effective traffic management, road safety, and intelligent transportation systems. In conclusion, this interdisciplinary approach, combining attention mechanisms, graph neural algorithms, and robotic technology, represents significant progress in traffic scene retrieval from remote sensing images, with potential applications in traffic management, road safety, and urban planning.

Knitting from Nature: Self-Sensing Soft Robotics Enabled by All-in-One Knit Architectures

Self-sensing soft robotics that mimic the proprioception and exteroception abilities of natural biological systems have shown great potential in challenging applications. However, current add-on strategies that simply combine sensors with actuators by post processing generally suffer from poor compatibility in mechanical properties, interfacing problems, complex manufacturing, and high cost. Herein, we present knitted soft robotics with build-in textile-integrated multimodal sensors, where the knit structure is used not only as a physical actuating layer but also as a sensing functional component. Based on different knit-stitch arrangements, an all-in-one knitted electronic skin with functions of neurons, sensing, and actuation in a single knit-structured fabric layer is constructed. The knitted electronic skin is then integrated into knitted soft robotics, enabling a proprioceptive sense of actuation deformation and an exteroceptive perception of ambient stimuli with minimized interferences for actuation. In addition, the tuck stitches serve as an anisotropic strain-limiting layer to increase the actuating energy efficiency, which resolves the key conflict of softness and volumetric power density in soft actuators. This design strategy provides a convenient, low-cost, and customized method to bring about structural and functional integrability into soft actuators, greatly extending the adaptability of current soft robotics for real-world applications.

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.

Highly Sensitive Fiber Pressure Sensors over a Wide Pressure Range Enabled by Resistive-Capacitive Hybrid Response

Soft capacitive pressure sensors with high performance are becoming increasingly in demand in the emerging flexible wearable field. While capacitive fiber pressure sensors have achieved high sensitivity, their sensitivity range is limited to low-pressure levels. As fiber sensors typically require preloading and fixation, this narrow range of high sensitivity poses a challenge for practical applications. To overcome this limitation, the study proposes resistive-capacitive hybrid response fiber pressure sensors (HFPSs) with three-layer core-sheath structures. The trigger and sensitivity enhancement mechanisms of the hybrid response are determined through model analysis and experimental verification. By adjustment of the sensitivity enhancement range of the hybrid response, the sensitivity attenuation of HFPSs is alleviated significantly. The obtained results demonstrate that HFPSs have excellent characteristics such as fast response, low hysteresis, wide response frequency, small signal drift, and good durability. The hybrid response enhances the practical sensitivity of HFPSs for various applications. With enhanced sensitivity, HFPSs can effectively monitor pulse signals at preloads ranging from 0 to 22.7 kPa. This wide range of preloads improves the fault tolerance of pulse monitoring and expands the potential application scenarios of fiber pressure sensors.

Thin-film PMUTs: a review of over 40 years of research

Thin-film PMUTs have been important research topics among microultrasound experts, and a concise review on their research progress is reported herein. Through rigorous surveying, scrutinization, and perception, it has been determined that the work in this field began nearly 44 years ago with the primitive development of functional piezoelectric thin-film materials. To date, there are three major companies commercializing thin-film PMUTs on a bulk scale. This commercialization illustrates the extensive contributions made by more than 70 different centers, research institutes, and agencies across 4 different continents regarding the vast development of these devices' design, manufacturing, and function. This review covers these important contributions in a short yet comprehensive manner; in particular, this paper educates readers about the global PMUT outlook, their governing design principles, their manufacturing methods, nonconventional yet useful PMUT designs, and category-wise applications. Crucial comparison charts of thin-film piezoelectric material used in PMUTs, and their categorically targeted applications are depicted and discussed to enlighten any MEMS designer who plans to work with PMUTs. Moreover, each relevant section features clear future predictions based on the author's past knowledge and expertise in this field of research and on the findings of a careful literature survey. In short, this review is a one-stop time-efficient guide for anyone interested in learning about these small devices.

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.

A High Sensitivity AlN-Based MEMS Hydrophone for Pipeline Leak Monitoring

In this work, a miniaturized, low-cost, low-power and high-sensitivity AlN-based micro-electro-mechanical system (MEMS) hydrophone is proposed for monitoring water pipeline leaks. The proposed MEMS Hydrophone consists of a piezoelectric micromachined ultrasonic transducer (PMUT) array, an acoustic matching layer and a pre-amplifier amplifier circuit. The array has 4 (2 × 2) PMUT elements with a first-order resonant frequency of 41.58 kHz. Due to impedance matching of the acoustic matching layer and the 40 dB gain of the pre-amplifier amplifier circuit, the packaged MEMS Hydrophone has a high sound pressure sensitivity of -170 ± 2 dB (re: 1 V/μPa). The performance with respect to detecting pipeline leaks and locating leak points is demonstrated on a 31 m stainless leaking pipeline platform. The standard deviation (STD) of the hydroacoustic signal and Monitoring Index Efficiency (MIE) are extracted as features of the pipeline leak. A random forest model is trained for accurately classifying the leak and no-leak cases using the above features, and the accuracy of the model is about 97.69%. The cross-correlation method is used to locate the leak point, and the localization relative error is about 10.84% for a small leak of 12 L/min.