Multiangle, self-powered sensor array for monitoring head impacts

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.

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.