Multiangle, self-powered sensor array for monitoring head impacts

TENG-Boosted Smart Sports with Energy Autonomy and Digital Intelligence

Technological advancements have profoundly transformed the sports domain, ushering it into the digital era. Services leveraging big data in intelligent sports-encompassing performance analytics, training statistical evaluations and metrics-have become indispensable. These tools are vital in aiding athletes with their daily training regimens and in devising sophisticated competition strategies, proving crucial in the pursuit of victory. Despite their potential, wearable electronic devices used for motion monitoring are subject to several limitations, including prohibitive cost, extensive energy usage, incompatibility with individual spatial structures, and flawed data analysis methodologies. Triboelectric nanogenerators (TENGs) have become instrumental in the development of self-powered devices/systems owing to their remarkable capacity to harnessing ambient high-entropy energy from the environment. This paper provides a thorough review of the advancements and emerging trends in TENG-based intelligent sports, focusing on physiological data monitoring, sports training performance, event refereeing assistance, and sports injury prevention and rehabilitation. Excluding the potential influence of sports psychological factors, this review provides a detailed discourse on present challenges and prospects for boosting smart sports with energy autonomy and digital intelligence. This study presents innovative insights and motivations for propelling the evolution of intelligent sports toward a more sustainable and efficient future for humanity.

Flexible Pressure Sensors Enhanced by 3D-Printed Microstructures

3D printing has revolutionized the development of flexible pressure sensors by enabling the precise fabrication of diverse microstructures that significantly enhance sensor performance. These advancements have substantially improved key attributes such as sensitivity, response time, and durability, facilitating applications in wearable electronics, robotics, and human-machine interfaces. This review provides a comprehensive analysis of the sensing mechanisms of these sensors, emphasizing the role of microstructures, such as micro-patterned, microporous, and hierarchical designs, in optimizing performance. The advantages of 3D printing techniques, including direct and indirect fabrication methods, in the creation of complex microstructures with high precision and adaptability are highlighted. Specific applications, including human physiological signal monitoring, motion detection, soft robotics, and emerging applications, are explored to demonstrate the versatility of these sensors. Additionally, this review briefly discusses key challenges, such as material compatibility, optimization difficulties, and environmental stability, as well as emerging trends, such as the integration of advanced technologies, innovative designs, and multidimensional sensing as promising avenues for future advancements. By summarizing recent progress and identifying opportunities for innovation, this review provides critical insights into bridging the gap between research and real-world applications, helping to accelerate the evolution of flexible pressure sensors with sophisticated 3D-printed microstructures.

Self-powered electrotactile textile haptic glove for enhanced human-machine interface

Human-machine interface (HMI) plays an important role in various fields, where haptic technologies provide crucial tactile feedback that greatly enhances user experience, especially in virtual reality/augmented reality, prosthetic control, and therapeutic applications. Through tactile feedback, users can interact with devices in a more realistic way, thereby improving the overall effectiveness of the experience. However, existing haptic devices are often bulky due to cumbersome instruments and power modules, limiting comfort and portability. Here, we introduce a concept of wearable haptic technology: a thin, soft, self-powered electrotactile textile haptic (SPETH) glove that uses the triboelectric effect and gas breakdown discharge for localized electrical stimulation. Daily hand movements generate sufficient mechanical energy to power the SPETH glove. Its features-softness, lightweight, self-sustainability, portability, and affordability-enable it to provide tactile feedback anytime and anywhere without external equipment. This makes the SPETH glove an enhanced, battery-free HMI suitable for a wide range of applications.

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.

Bio-inspired e-skin with integrated antifouling and comfortable wearing for self-powered motion monitoring and ultra-long-range human-machine interaction

Electronic skin (e-skin) inspired by the sensory function of the skin demonstrates broad application prospects in health, medicine, and human-machine interaction. Herein, we developed a self-powered all-fiber bio-inspired e-skin (AFBI E-skin) that integrated functions of antifouling, antibacterial, biocompatibility and breathability. AFBI E-skin was composed of three layers of electrospun nanofibrous films. The superhydrophobic outer layer Poly(vinylidene fluoride)-silica nanofibrous films (PVDF-SiO2 NFs) possessed antifouling properties against common liquids in daily life and resisted bacterial adhesion. The polyaniline nanofibrous films (PANI NFs) were used as the electrode layer, and it had strong "static" antibacterial capability. Meanwhile, the inner layer Polylactic acid nanofibrous films (PLA NFs) served as a biocompatible substrate. Based on the triboelectric nanogenerator principle, AFBI E-skin not only enabled self-powered sensing but also utilized the generated electrical stimulation for "dynamic" antibacterial. The "dynamic-static" synergistic antibacterial strategy greatly enhanced the antibacterial effect. AFBI E-skin could be used for self-powered motion monitoring to obtain a stable signal output even when water was splashed on its surface. Finally, based on AFBI E-skin, we constructed an ultra-long-range human-machine interaction control system, enabling synchronized hand gestures between human hand and robotic hand in any internet-covered area worldwide theoretically. AFBI E-skin exhibited vast application potential in fields like smart wearable electronics and intelligent robotics.

Triboelectric sensor gloves for real-time behavior identification and takeover time adjustment in conditionally automated vehicles

The takeover issue, especially the setting of the takeover time budget, is a critical factor restricting the implementation and development of conditionally automated vehicles. The general fixed takeover time budget has certain limitations, as it does not take into account the driver's non-driving behaviors. Here, we propose an intelligent takeover assistance system consisting of all-round sensing gloves, a non-driving behavior identification module, and a takeover time budget determination module. All-round sensing gloves based on triboelectric sensors seamlessly detect delicate motions of hands and interactions between hands and other objects, and then transfer the electrical signals to the non-driving behavior identification module, which achieves an accuracy of 94.72% for six non-driving behaviors. Finally, combining the identification result and its corresponding minimum takeover time budget obtained through the takeover time budget determination module, our system dynamically adjusts the takeover time budget based on the driver's current non-driving behavior, significantly improving takeover performance in terms of safety and stability. Our work presents a potential value in the application and implementation of conditionally automated vehicles.

Mechano-Graded Contact-Electrification Interfaces Based Artificial Mechanoreceptors for Robotic Adaptive Reception

Triboelectrification-based artificial mechanoreceptors (TBAMs) is able to convert mechanical stimuli directly into electrical signals, realizing self-adaptive protection and human-machine interactions of robots. However, traditional contact-electrification interfaces are prone to reaching their deformation limits under large pressures, resulting in a relatively narrow linear range. In this work, we fabricated mechano-graded microstructures to modulate the strain behavior of contact-electrification interfaces, simultaneously endowing the TBAMs with a high sensitivity and a wide linear detection range. The presence of step regions within the mechanically graded microstructures helps contact-electrification interfaces resist fast compressive deformation and provides a large effective area. The highly sensitive linear region of TBAM with 1.18 V/kPa can be effectively extended to four times of that for the devices with traditional interfaces. In addition, the device is able to maintain a high sensitivity of 0.44 V/kPa even under a large pressure from 40 to 600 kPa. TBAM has been successfully used as an electronic skin to realize self-adaptive protection and grip strength perception for a commercial robot arm. Finally, a high angle resolution of 2° and an excellent linearity of 99.78% for joint bending detection were also achieved. With the aid of a convolutional neural network algorithm, a data glove based on TBAMs realizes a high accuracy rate of 95.5% for gesture recognition in a dark environment.

Self-Powered, Flexible, Transparent Tactile Sensor Integrating Sliding and Proximity Sensing

Tactile sensing is currently a research hotspot in the fields of intelligent perception and robotics. The method of converting external stimuli into electrical signals for sensing is a very effective strategy. Herein, we proposed a self-powered, flexible, transparent tactile sensor integrating sliding and proximity sensing (SFTTS). The principle of electrostatic induction and contact electrification is used to achieve tactile response when external objects approach and slide. Experiments show that the material type, speed, and pressure of the perceived object can cause the changes of the electrical signal. In addition, fluorinated ethylene propylene (FEP) is used as the contact electrification layer, and indium tin oxide (ITO) is used as the electrostatic induction electrode to achieve transparency and flexibility of the entire device. By utilizing the transparency characteristics of this sensor to integrate with optical cameras, it is possible to achieve integrated perception of tactile and visual senses. This has great advantages for applications in the field of intelligent perception and is expected to be integrated with different types of optical sensors in the future to achieve multimodal intelligent perception and sensing technology, which will contribute to the intelligence and integration of robot sensing.

Triboelectric sensor with ultra-wide linear range based on water-containing elastomer and ion-rich interface

The incompatibility of the high sensitivity and wide linear range still restricts the further development of active sensors. Here we report a triboelectric pressure sensor based on water-containing triboelectric elastomer with gradient-based microchannels. Tiny amount of liquid is injected into the triboelectric elastomer and the pressure-induced water bridges can modulate the built-in electric field of the sensor, which enhance the signal linearity near the compression limit. Moreover, it has been found that liquid-solid contact electrification can be enhanced by triggering selective ionic transfer, while the prepared ion-rich interface in the microchannels boosts the sensitivity of the sensor. Hence, an ultra-wide linear range (5 kPa-1240 kPa) with a sensitivity of 0.023 V kPa-1 can be achieved, which is so far the widest linear range of active sensors to our knowledge. Our work can promote the practical application of triboelectric sensors and provide new insights for other sensory devices.

Multi-Attribute Triboelectric Materials and Innovative Applications Via TENGs

Triboelectric nanogenerators (TENGs) as an avant-garde technology that transforms mechanical energy into electrical energy, offering a new direction for green energy and sustainable development. By means of high-efficiency TENGs, conventional materials as new triboelectric materials have exhibited multi-attribute characteristics, achieving innovative applications in the field of micro-nano energy harvesting and self-powered sensing. The progress of TENGs technology with the triboelectric materials is complementary and mutually promoting. On the one hand, one of the cruxes of TENGs lies in the triboelectric materials, which have a decisive impact on their performance. On the other hand, as the research and application of TENGs continue to deepen, higher demands are placed on triboelectric materials, which in turn promotes the advancement of the entire material system as well as the fields of materials science and physics. This work aims to delve into the characteristics, types, preferred choices, and modification treatments of triboelectric materials on the performances of TENGs, hoping to provide guidance and insights for future research and applications.

Additively manufactured micro-lattice dielectrics for multiaxial capacitive sensors

Soft sensors that can perceive multiaxial forces, such as normal and shear, are of interest for dexterous robotic manipulation and monitoring of human performance. Typical planar fabrication techniques have substantial design constraints that often prohibit the creation of functionally compelling and complex architectures. Moreover, they often require multiple-step operations for production. Here, we use an additive manufacturing process based on continuous liquid interface production to create high-resolution (30-micrometer) three-dimensional elastomeric polyurethane lattices for use as dielectric layers in capacitive sensors. We show that the capacitive responses and sensitivities are highly tunable through designs of lattice type, thickness, and material-void volume percentage. Microcomputed tomography and finite element simulation are used to elucidate the influence of lattice design on the deformation mechanism and concomitant sensing behavior. The advantage of three-dimensional printing is exhibited with examples of fully printed representative athletic equipment with integrated sensors.

A Study on the Mechanisms and Performance of a Polyvinyl Alcohol-Based Nanogenerator Based on the Triboelectric Effect

Polyvinyl alcohol (PVA), a versatile polymer, is extensively used across many industries, such as chemicals, food, healthcare, textiles, and packaging. However, research on applying PVA to triboelectric nanogenerators (TENGs) remains limited. Consequently, we chose PVA as the primary material to explore its contact electrification mechanisms at the molecular level, alongside materials like Polyethylene (PE), Polyvinylidene fluoride (PVDF), and Polytetrafluoroethylene (PTFE). Our findings show that PVA has the highest band gap, with the smallest band gap occurring between the HOMO of PVA and the LUMO of PTFE. During molecular contact, electron transfer primarily occurs in the outermost layers of the molecules, influenced by the functional groups of the polymers. The presence of fluorine atoms enhances the electron transfer between PVA and PTFE to maximum levels. Experimental validation confirmed that PVA and PTFE contact yields the highest triboelectric performance: VOC of 128 V, ISC of 2.83 µA, QSC of 82 nC, and an output power of 384 µW. Moreover, P-TENG, made of PVA and PTFE, was successfully applied in self-powered smart devices and monitored human respiration and bodily movements effectively. These findings offer valuable insights into using PVA in triboelectric nanogenerator technologies.

AI-Assisted Self-Powered Vehicle-Road Integrated Electronics for Intelligent Transportation Collaborative Perception

Collaborative perception between a vehicle and the road has the potential to enhance the limited perception capability of autonomous driving technologies. With this background, self-powered vehicle-road integrated electronics (SVRIE) with a multilevel fractal structure is designed to play a dual role, including a SVRIE device integrated into vehicle tires and a SVRIE array embedded into a road surface. The pressure sensing capability and anti-crosstalk performance of the SVRIE array are characterized separately to validate the feasibility of applying the SVRIE in a cooperative vehicle-infrastructure system. It is demonstrated that the SVRIE based on the multi-layered fractal structure exhibits maximum performance in collaborative sensing and interaction between vehicles and road information, such as vehicle motion, road surface condition, and tire life cycle health monitoring. Traditional data analysis methods are often of questionable accuracy. Therefore, a convolutional neural network is used to classify the vehicle and road conditions with accuracy of at least 88.3%. The transfer learning model is constructed to enhance the road surface identification capabilities with 100% accuracy. The accuracies of the vehicle tire motion recognition and tire health monitoring are 97% and 99%, respectively. This work provides new ideas for collaborative perception between vehicles and roadsides.

Achieving Ultrahigh DC-Power Triboelectric Nanogenerators by Lightning Rod-Inspired Field Emission Modeling

Direct current triboelectric nanogenerators (DC-TENGs) are a groundbreaking technology to capture micromechanical energy from the natural environment, which is crucial for directly powering sensor networks. However, the research bottleneck in enhancing the triboelectric electrification capability and charge storage capability of dielectrics has hindered the overall performance breakthroughs of the DC-TENG. Here, a field emission model-based DC-TENG (FEM-TENG) is proposed, inspired by lightning rods. The enhanced local electric field between dielectric materials and electrodes induces strong electron tunneling, which improves charge neutralization on the surface of materials and their internal charge storage space, thereby utilizing the dielectric volume effect effectively and strengthening triboelectricity. Guided by the field emission model, the FEM-TENG with a historic crest factor of 1.00375 achieves a groundbreaking record of an average power density of 16.061 W m-2 Hz-1 (1,591 W m-3 Hz-1), which is 5.36-fold of the latest DC-TENG. In particular, the FEM-TENG with high durability (100%) truly realizes the collection of breeze energy and continuously drives 50 thermohygrometers. Four additional applications exemplify the FEM-TENG, enabling comprehensive sensing of land, water, and air. This work proposes a paradigm strategy for the in-depth utilization of dielectric films, aiming to enhance the output power of DC-TENGs.

Toward the Next Generation Human-Machine Interaction: Headworn Wearable Devices

Wearable devices are lightweight and portable devices worn directly on the body or integrated into the user's clothing or accessories. They are usually connected to the Internet and combined with various software applications to monitor the user's physical conditions. The latest research shows that wearable head devices, particularly those incorporating microfluidic technology, enable the monitoring of bodily fluids and physiological states. Here, we summarize the main forms, functions, and applications of head wearable devices through innovative researches in recent years. The main functions of wearable head devices are sensor monitoring, diagnosis, and even therapeutic interventions. Through this application, real-time monitoring of human physiological conditions and noninvasive treatment can be realized. Furthermore, microfluidics can realize real-time monitoring of body fluids and skin interstitial fluid, which is highly significant in medical diagnosis and has broad medical application prospects. However, despite the progress made, significant challenges persist in the integration of microfluidics into wearable devices at the current technological level. Herein, we focus on summarizing the cutting-edge applications of microfluidic contact lenses and offer insights into the burgeoning intersection between microfluidics and head-worn wearables, providing a glimpse into their future prospects.

Bioinspired Suspended Sensing Membrane Array with Modulable Wedged-Conductive Channels for Crosstalk-Free and High-Resolution Detection

High spatial-resolution detection is essential for biomedical applications and human-machine interaction. However, as the sensor array density increases, the miniaturization will lead to interference between adjacent units and deterioration in sensing performance. Here, inspired by the cochlea's sensing structure, a high-density flexible pressure sensor array featuring with suspended sensing membrane with sensitivity-enhanced customized channels is presented for crosstalk-free and high-resolution detection. By imitating the basilar membrane attached to spiral ligaments, a sensing membrane is fixed onto a high-stiffness substrate with cavities, forming a stable braced isolation to provide an excellent crosstalk-free capability (crosstalk coefficient: 47.24 dB) with high-density integration (100 units within 1 cm2). Similar to the opening of ion channels in hair cells, the wedge-type expansion of the embedded cracks introduced by stress concentration structures enables a high sensitivity (0.19 kPa-1) and a large measuring range (400 kPa). Finally, it demonstrates promising applications in distributed displays and the condition monitoring of medical-surgical intubation.

Light-Boosting Highly Sensitive and Ultrafast Piezoelectric Sensor Based on Composite Membrane of Copper Phthalocyanine and Graphene Oxide

Self-powered wearable pressure sensors based on flexible electronics have emerged as a new trend due to the increasing demand for intelligent and portable devices. Improvements in pressure-sensing performance, including in the output voltage, sensitivity and response time, can greatly expand their related applications; however, this remains challenging. Here, we report on a highly sensitive piezoelectric sensor with novel light-boosting pressure-sensing performance, based on a composite membrane of copper phthalocyanine (CuPC) and graphene oxide (GO) (CuPC@GO). Under light illumination, the CuPC@GO piezoelectric sensor demonstrates a remarkable increase in output voltage (381.17 mV, 50 kPa) and sensitivity (116.80 mV/kPa, <5 kPa), which are approximately twice and three times of that the sensor without light illumination, respectively. Furthermore, light exposure significantly improves the response speed of the sensor with a response time of 38.04 µs and recovery time of 58.48 µs, while maintaining excellent mechanical stability even after 2000 cycles. Density functional theory calculations reveal that increased electron transfer from graphene to CuPC can occur when the CuPC is in the excited state, which indicates that the light illumination promotes the electron excitation of CuPC, and thus brings about the high polarization of the sensor. Importantly, these sensors exhibit universal spatial non-contact adjustability, highlighting their versatility and applicability in various settings.

An ultra-soft conductive elastomer for multifunctional tactile sensors with high range and sensitivity

Flexible tactile sensors have become important as essential tools for facilitating human and object interactions. However, the materials utilized for the electrodes of capacitive tactile sensors often cannot simultaneously exhibit high conductivity, low modulus, and strong adhesiveness. This limitation restricts their application on flexible interfaces and results in device failure due to mechanical mismatch. Herein, we report an ultra-low modulus, highly conductive, and adhesive elastomer and utilize it to fabricate a microstructure-coupled multifunctional flexible tactile sensor. We prepare a supramolecular conductive composite film (SCCF) as the electrode of the tactile sensor using a supramolecular deep eutectic solvent, polyvinyl alcohol (PVA) solution, poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), and MXene suspension. We employ a polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) film containing 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMIM:TFSI) as the dielectric layer to fabricate capacitive sensors with an electrical double layer structure. Furthermore, we enhance the performance of the device by incorporating coupled pyramid and dome microstructures, which endow the sensor with multi-directional force detection. Our SCCF exhibits extremely high conductivity (reaching 710 S cm-1), ultra-low modulus (0.8 MPa), and excellent interface adhesion strength (>120 J m-2). Additionally, due to the outstanding conductivity and unique structure of the SCCF, it possesses remarkable electromagnetic shielding ability (>50 dB). Moreover, our device demonstrates a high sensitivity of up to 1756 kPa-1 and a wide working range reaching 400 kPa, combining these attributes with the requirements of an ultra-soft human-machine interface to ensure optimal contact between the sensor and interface materials. This innovative and flexible tactile sensor holds great promise and potential for addressing various and complex demands of human-machine interaction.

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.

Large-area, untethered, metamorphic, and omnidirectionally stretchable multiplexing self-powered triboelectric skins

Large-area metamorphic stretchable sensor networks are desirable in haptic sensing and next-generation electronics. Triboelectric nanogenerator-based self-powered tactile sensors in single-electrode mode constitute one of the best solutions with ideal attributes. However, their large-area multiplexing utilizations are restricted by severe misrecognition between sensing nodes and high-density internal circuits. Here, we provide an electrical signal shielding strategy delivering a large-area multiplexing self-powered untethered triboelectric electronic skin (UTE-skin) with an ultralow misrecognition rate (0.20%). An omnidirectionally stretchable carbon black-Ecoflex composite-based shielding layer is developed to effectively attenuate electrostatic interference from wirings, guaranteeing low-level noise in sensing matrices. UTE-skin operates reliably under 100% uniaxial, 100% biaxial, and 400% isotropic strains, achieving high-quality pressure imaging and multi-touch real-time visualization. Smart gloves for tactile recognition, intelligent insoles for gait analysis, and deformable human-machine interfaces are demonstrated. This work signifies a substantial breakthrough in haptic sensing, offering solutions for the previously challenging issue of large-area multiplexing sensing arrays.

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.

Progress in wearable acoustical sensors for diagnostic applications

With extensive and widespread uses of miniaturized and intelligent wearable devices, continuously monitoring subtle spatial and temporal changes in human physiological states becomes crucial for daily healthcare and professional medical diagnosis. Wearable acoustical sensors and related monitoring systems can be comfortably applied onto human body with a distinctive function of non-invasive detection. This paper reviews recent advances in wearable acoustical sensors for medical applications. Structural designs and characteristics of the structural components of wearable electronics, including piezoelectric and capacitive micromachined ultrasonic transducer (i.e., pMUT and cMUT), surface acoustic wave sensors (SAW) and triboelectric nanogenerators (TENGs) are discussed, along with their fabrication techniques and manufacturing processes. Diagnostic applications of these wearable sensors for detection of biomarkers or bioreceptors and diagnostic imaging have further been discussed. Finally, main challenges and future research directions in these fields are highlighted.