Anatomically Designed Triboelectric Wristbands with Adaptive Accelerated Learning for Human-Machine Interfaces

Toward an AI Era: Advances in Electronic Skins

Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.

Hoffmeister Effect Optimized Hydrogel Electrodes with Enhanced Electrical and Mechanical Properties for Nerve Conduction Studies

Flexible epidermal electrodes hold substantial promise in realizing human electrophysiological information collections. Conventional electrodes exhibit certain limitations, including the requirement of skin pretreatment, reliance on external object-assisted fixation, and a propensity of dehydration, which severely hinder their applications in medical diagnosis. To tackle those issues, we developed a hydrogel electrode with both transcutaneous stimulation and neural signal acquisition functions. The electrode consists of a composite conductive layer (CCL) and adhesive conductive hydrogel (ACH). The CCL is designed as a laminated structure with high conductivity and charge storage capacity (CSC). Based on the optimization of Hoffmeister effect, the ACH demonstrates excellent electrical (resistivity of 3.56 Ω·m), mechanical (tensile limit of 1,650%), and adhesion properties (peeling energy of 0.28 J). The utilization of ACH as electrode/skin interface can reduce skin contact impedance and noise interference and enhance the CSC and charge injection capacity of electrodes. As a proof of concept, peripheral nerve conduction studies were performed on human volunteers to evaluate the as-fabricated hydrogel electrodes. Compared with the commercial electrodes, our hydrogel electrodes achieved better signal continuity and lower distortion, higher signal-to-noise ratio (~35 dB), and lower stimulation voltages (up to 27% lower), which can improve the safety and comfort of nerve conduction studies.

Channel Selection for Gesture Recognition Using Force Myography: A Universal Model for Gesture Measurement Points

Gesture recognition has emerged as a significant research domain in computer vision and human-computer interaction. One of the key challenges in gesture recognition is how to select the most useful channels that can effectively represent gesture movements. In this study, we have developed a channel selection algorithm that determines the number and placement of sensors that are critical to gesture classification. To validate this algorithm, we constructed a Force Myography (FMG)-based signal acquisition system. The algorithm considers each sensor as a distinct channel, with the most effective channel combinations and recognition accuracy determined through assessing the correlation between each channel and the target gesture, as well as the redundant correlation between different channels. The database was created by collecting experimental data from 10 healthy individuals who wore 16 sensors to perform 13 unique hand gestures. The results indicate that the average number of channels across the 10 participants was 3, corresponding to an 75% decrease in the initial channel count, with an average recognition accuracy of 94.46%. This outperforms four widely adopted feature selection algorithms, including Relief-F, mRMR, CFS, and ILFS. Moreover, we have established a universal model for the position of gesture measurement points and verified it with an additional five participants, resulting in an average recognition accuracy of 96.3%. This study provides a sound basis for identifying the optimal and minimum number and location of channels on the forearm and designing specialized arm rings with unique shapes.

Skin-Inspired Multi-Modal Mechanoreceptors for Dynamic Haptic Exploration

Active sensing is a fundamental aspect of human and animal interactions with the environment, providing essential information about the hardness, texture, and tackiness of objects. This ability stems from the presence of diverse mechanoreceptors in the skin, capable of detecting a wide range of stimuli and from the sensorimotor control of biological mechanisms. In contrast, existing tactile sensors for robotic applications typically excel in identifying only limited types of information, lacking the versatility of biological mechanoreceptors and the requisite sensing strategies to extract tactile information proactively. Here, inspired by human haptic perception, a skin-inspired artificial 3D mechanoreceptor (SENS) capable of detecting multiple mechanical stimuli is developed to bridge sensing and action in a closed-loop sensorimotor system for dynamic haptic exploration. A tensor-based non-linear theoretical model is established to characterize the 3D deformation (e.g., tensile, compressive, and shear deformation) of SENS, providing guidance for the design and optimization of multimode sensing properties with high fidelity. Based on SENS, a closed-loop robotic system capable of recognizing objects with improved accuracy (≈96%) is further demonstrated. This dynamic haptic exploration approach shows promise for a wide range of applications such as autonomous learning, healthcare, and space and deep-sea exploration.

A Novel Mechano-Synthesized Zeolitic Tetrazolate Framework for a High-Performance Triboelectric Nanogenerator and Self-Powered Selective Neurochemical Detection

Designing a high-performing triboelectric novel material with eco-friendly, rapid, and cost-effective synthesis is the future of material research in triboelectric nanogenerators (TENG). We report a mechanochemical ball mill synthesis of a zeolitic tetrazolate framework (ZTF-8) that is isostructural with the well-known zeolitic imidazolate framework ZIF-8. ZTF-8 is extremely stable in water, 0.1 M aqueous acid/base solutions for 75 days at 25 °C, and boiling water (100 °C) for 7 days. Kelvin probe force microscopy and molecular electrostatic surface potential computational analysis exhibited that ZTF-8 has a very high positive surface potential. Atomic force microscopy and three-dimensional digital microscopy studies reveal the high roughness profile in the ZTF-8 film. The unique structure, exceptional acid/base stability, good dielectric property, and high roughness profile combined with the extremely electropositive nature of ZTF-8 make it a suitable candidate as a polymer-free triboelectric positive material in TENG with outstanding performance (power density of 720 mW/m2). High triboelectric output was further validated using the COMSOL Multiphysics simulation tool. Simple mechanical hand tapping of the ZTF-based TENG (ZTF-TENG) device generates high electric output, which was practically used to power numerous low-powered devices like tally counter, clinical thermometer, and digital clock and also illuminates 125 light-emitting diodes. In addition, the efficiency of ZTF-TENG was utilized as a self-powered device for a selective dopamine (DA) sensor with good sensitivity (377.76 mV/μM/cm2), wide range linearity (5-120 μM), and excellent limit of detection (0.42 μM).

Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion

In the smart era, big data analysis based on sensor units is important in intelligent motion. In this study, a dance sports and injury monitoring system (DIMS) based on a recyclable flexible triboelectric nanogenerator (RF-TENG) sensor module, a data processing hardware module, and an upper computer intelligent analysis module are developed to promote intelligent motion. The resultant RF-TENG exhibits an ultra-fast response time of 17 ms, coupled with robust stability demonstrated over 4200 operational cycles, with 6% variation in output voltage. The DIMS enables immersive training by providing visual feedback on sports status and interacting with virtual games. Combined with machine learning (K-nearest neighbor), good classification results are achieved for ground-jumping techniques. In addition, it shows some potential in sports injury prediction (i.e., ankle sprains, knee hyperextension). Overall, the sensing system designed in this study has broad prospects for future applications in intelligent motion and healthcare.

Biomimetic Electronic Skin for Robots Aiming at Superior Dynamic-Static Perception and Material Cognition Based on Triboelectric-Piezoresistive Effects

Empowering robots with tactile perception and even thinking as well as judgment capabilities similar to those of humans is an inevitable path for the development of future robots. Here, we propose a biomimetic electronic skin (BES) that truly serves and applies to robots to achieve superior dynamic-static perception and material cognition functionalities. First, the microstructured triboelectric and piezoresistive layers are fabricated by a facile template method followed by selected self-polymerization treatment, enabling BES with high sensitivity and a wide detection range. Further, through laminated-independent triboelectric and piezoresistive parts for perceiving dynamic and static pressures simultaneously, the BES is capable of supporting the robot hand to monitor the entire process during object grasping. Most importantly, by further combining a neural network model, an intelligent cognition system is constructed for real-time cognition of the object material species via one touch of the robot hand under arbitrary pressures, which goes beyond the human cognition ability.

AR-Enabled Persistent Human-Machine Interfaces via a Scalable Soft Electrode Array

Augmented reality (AR) is a computer graphics technique that creates a seamless interface between the real and virtual worlds. AR usage rapidly spreads across diverse areas, such as healthcare, education, and entertainment. Despite its immense potential, AR interface controls rely on an external joystick, a smartphone, or a fixed camera system susceptible to lighting. Here, an AR-integrated soft wearable electronic system that detects the gestures of a subject for more intuitive, accurate, and direct control of external systems is introduced. Specifically, a soft, all-in-one wearable device includes a scalable electrode array and integrated wireless system to measure electromyograms for real-time continuous recognition of hand gestures. An advanced machine learning algorithm embedded in the system enables the classification of ten different classes with an accuracy of 96.08%. Compared to the conventional rigid wearables, the multi-channel soft wearable system offers an enhanced signal-to-noise ratio and consistency over multiple uses due to skin conformality. The demonstration of the AR-integrated soft wearable system for drone control captures the potential of the platform technology to offer numerous human-machine interface opportunities for users to interact remotely with external hardware and software.

Bulk Schottky Junctions-Based Flexible Triboelectric Nanogenerators to Power Backscatter Communications in Green 6G Networks

This work introduces a novel method to construct Schottky junctions to boost the output performance of triboelectric nanogenerators (TENGs). Perovskite barium zirconium titanate (BZT) core/metal silver shell nanoparticles are synthesized to be embedded into electrospun polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) nanofibers before they are used as tribo-negative layers. The output power of TENGs with composite fiber mat exhibited >600% increase compared to that with neat polymer fiber mat. The best TENG achieved 1339 V in open-circuit voltage, 40 µA in short-circuit current and 47.9 W m-2 in power density. The Schottky junctions increased charge carrier density in tribo-layers, ensuring a high charge transfer rate while keeping the content of conductive fillers low, thus avoiding charge loss and improving performance. These TENGs are utilized to power radio frequency identification (RFID) tags for backscatter communication (BackCom) systems, enabling ultra-massive connectivity in the 6G wireless networks and reducing information communications technology systems' carbon footprint. Specifically, TENGs are used to provide an additional energy source to the passive tags. Results show that TENGs can boost power for BackCom and increase the communication range by 386%. This timely contribution offers a novel route for sustainable 6G applications by exploiting the expanded communication range of BackCom tags.

Dome-Conformal Electrode Strategy for Enhancing the Sensitivity of BaTiO3-Doped Flexible Self-powered Triboelectric Pressure Sensor

A microstructured surface has been applied in self-powered triboelectric pressure sensors to increase the charge-carrying sites and enhance the output performance. However, the microstructure increases the distance between the electrode and the triboelectric layer, and its influence on the output performance is unknown. Herein, we proposed a dome-conformal electrode strategy for a self-powered triboelectric nanogenerator (TENG) pressure sensor. With a simple reverse-dome adsorption process, an ultrathin triboelectric layer and Ag electrode can be made conformal to the dome PDMS structure. The TENG sensor is constructed with paper as a positive triboelectric layer. Compared with the device based on nonconformal structure, the conformal design strategy endows the device with a faster charge transfer and enhanced output voltage. By doping with BaTiO3, the outermost triboelectric layer can be easily modified to improve its ability of sustaining charge, and an ultrathin PDMS layer is coated on the triboelectric layer to expand the triboelectric polarity difference between two triboelectric layers so as to enhance the output voltage. The synergistic effects enable the optimized TENG sensor with a sensitivity of 0.75 V/kPa in the low-pressure region (0-26 kPa) and 0.19 V/kPa in the high-pressure range (26-120 kPa). Its application in human motion detection, grabbing water beakers, and noncontact distance testing has been demonstrated. This work provides a route such as a conformal structure design strategy to enhance the output performance of a microstructure-based TENG sensor.

Harvesting Inertial Energy and Powering Wearable Devices: A Review

Amidst the swift progression of microelectronics and Internet of Things technology, wearable devices are gradually gaining ground in the domains of human health monitoring. Recently, human bioenergy harvesting has emerged as a plausible alternative to batteries. This paper delves into harvesting human inertial energy that stimulates inertial masses through human motion and then transmutes the motion of the inertial masses into electrical energy. The inertial energy harvester is better suited for low-frequency and irregular human motion. This review first identifies the sources of human motion excitation that are compatible with inertial energy harvesters and then provides a summary of the operating principles and the comparisons of the commonly used energy conversion mechanisms, including electromagnetic, piezoelectric, and triboelectric transducers. The review thoroughly summarizes the latest advancements in human inertial energy-harvesting technology that are categorized and grouped based on their excitation sources and mechanical modulation methods. In addition, the review outlines the applications of inertial energy harvesters in powering wearable devices, medical health monitoring, and as mobile power sources. Finally, the challenges faced by inertial energy-harvesting technologies are discussed, and the review provides a perspective on the potential developments in the field.

Facile Fabrication of "Tacky", Stretchable, and Aligned Carbon Nanotube Sheet-Based Electronics for On-Skin Health Monitoring

Point-of-care monitoring of physiological signals such as electrocardiogram, electromyogram, and electroencephalogram is essential for prompt disease diagnosis and quick treatment, which can be realized through advanced skin-worn electronics. However, it is still challenging to design an intimate and nonrestrictive skin-contact device for physiological measurements with high fidelity and artifact tolerance. This research presents a facile method using a "tacky" surface to produce a tight interface between the ACNT skin-like electronic and the skin. The method provides the skin-worn electronic with a stretchability of up to 70% strain, greater than that of most common epidermal electrodes. Low-density ACNT bundles facilitate the infiltration of adhesive and improve the conformal contact between the ACNT sheet and the skin, while dense ACNT bundles lessen this effect. The stretchability and conformal contact allow the ACNT sheet-based electronics to create a tight interface with the skin, which enables the high-fidelity measurement of physiological signals (the Pearson's coefficient of 0.98) and tolerance for motion artifacts. In addition, our method allows the use of degradable substrates to enable reusability and degradability of the electronics based on ACNT sheets, integrating "green" properties into on-skin electronics.