Intelligent Wearables Research Articles

Supercapacitive Multimode Sensing with Tunable Graphene Sheet Film Electrodes

AbstractSupercapacitive multimode sensing is gaining significant attention across diverse domains, particularly within burgeoning fields of intelligent wearables and human‐computer interaction. Typically, these sensors are constructed using a matrix of conductive nanowires and soft materials such as polydimethylsiloxane (PDMS), which serve as electrodes in thin film supercapacitors (TFSs). To facilitate multimode sensing, ability to modulate response curves is crucial for practical application. In this study, we introduce porous conducting graphene sheet (GS) films with customizable thickness as TFS electrodes. We first develop a wafer‐scale fabrication method using vacuum filtration, to yield GS films with adjustable sheet resistance that can approach the percolation threshold. The use of a hydrophilic cellulose acetate (CA) filter membrane facilitates spontaneuous and nondestructive detachment of the GS film from the membrane post‐dehydration. The spontaneous separation mechanism is elucidated through time‐dependent contact angle measurements on porous films. The GS film, once separated, can be seamlessly and consistently incorporated into TFS devices as electrodes. This integration allows for multimode sensing capability with simultaneous capacitive strain sensing and ionotropic temperature sensing. The tunable nature of these porous GS films by adjusting the electrode thickness, enables fine‐tuning of the response curves for multimode sensing.

Hierarchically porous architectured stretchable fibrous materials in energy harvesting and self-powered sensing

Stretchable electronic textiles have integrated seamlessly into our lives, promising advancements in health monitoring and intelligent wearables. However, one key challenge is to combine fibrous materials and functional materials to realize an integrity of stretchability and functionality by a simple and scalable method. To address this challenge, we propose a novel hierarchically buckled porous microstructure fabric (HBPMFs) via a surface self-assembly method, which can integrate with customized functional nanoparticles for tailorable wearable energy devices. The unique buckled porous microstructure endows HBPMFs with remarkable porous stretchability and high loading capacity of functional nanoparticles. Based on HBPMFs, triboelectric nanogenerators (HBPMFs-TENGs) with high structural integrity, stretchability and enhanced electrical outputs are design and fabricated. The as-made Ag@HBPMFs-TENG exhibits a greater enhancement in voltage by 4 times than the original fabric TENG, which can drive miniature wearable electronics. The features enable the Ag@HBPMFs-TENG in efficiently powering wearable electronics and acting as a self-powered sensor capable of detecting human motions. This work will offer a new insight for porous stretchable energy devices by designing unique microstructural surfaces on commercial fibrous materials.

Dynamically predicting comprehension difficulties through physiological data and intelligent wearables

Comprehending digital content written in natural language online is vital for many aspects of life, including learning, professional tasks, and decision-making. However, facing comprehension difficulties can have negative consequences for learning outcomes, critical thinking skills, decision-making, error rate, and productivity. This paper introduces an innovative approach to predict comprehension difficulties at the local content level (e.g., paragraphs). Using affordable wearable devices, we acquire physiological responses non-intrusively from the autonomous nervous system, specifically pulse rate variability, and electrodermal activity. Additionally, we integrate data from a cost-effective eye-tracker. Our machine learning algorithms identify ’hotspots’ within the content and regions corresponding to a high cognitive load. These hotspots represent real-time predictors of comprehension difficulties. By integrating physiological data with contextual information (such as the levels of experience of individuals), our approach achieves an accuracy of 72.11% ± 2.21, a precision of 0.77, a recall of 0.70, and an f1 score of 0.73. This study opens possibilities for developing intelligent, cognitive-aware interfaces. Such interfaces can provide immediate contextual support, mitigating comprehension challenges within content. Whether through translation, content generation, or content summarization using available Large Language Models, this approach has the potential to enhance language comprehension.

Emerging Trends of Nanofibrous Piezoelectric and Triboelectric Applications: Mechanisms, Electroactive Materials, and Designed Architectures.

Over the past few decades, significant progress in piezo-/triboelectric nanogenerators (PTEGs) has led to the development of cutting-edge wearable technologies. Nanofibers with good designability, controllable morphologies, large specific areas, and unique physicochemical properties provide a promising platform for PTEGs for various advanced applications. However, the further development of nanofiber-based PTEGs is limited by technical difficulties, ranging from materials design to device integration. Herein, the current developments in PTEGs based on electrospun nanofibers are systematically reviewed. This review begins with the mechanisms of PTEGs and the advantages of nanofibers and nanodevices, including high breathability, waterproofness, scalability, and thermal-moisture comfort. In terms of materials and structural design, novel electroactive nanofibers and structure assemblies based on 1D micro/nanostructures, 2D bionic structures, and 3D multilayered structures are discussed. Subsequently, nanofibrous PTEGs in applications such as energy harvesters, personalized medicine, personal protective equipment, and human-machine interactions are summarized. Nanofiber-based PTEGs still face many challenges such as energy efficiency, material durability, device stability, and device integration. Finally, the research gap between research and practical applications of PTEGs is discussed, and emerging trends are proposed, providing some ideas for the development of intelligent wearables.

Intelligent Wearable Systems: Opportunities and Challenges in Health and Sports

Wearable devices, or wearables, designed to be attached to the human body, can gather personalized real-time data and continuously monitor an individual’s health status and physiological disposition in a non-invasive manner. Intelligent wearables integrate advanced machine learning algorithms to process complex data patterns and provide accurate insights. As a result, intelligent wearables have emerged as a ground-breaking innovation in the fields of sports and health, introducing a new paradigm in kinematic analysis and patient data evaluation. For example, virtual coaches offer feedback on athletes’ performance, whereas virtual physicians assist in customizing medication for patients. This article provides an overview of various types of intelligent wearables and their applications in health and sports, categorizes machine learning algorithms, and introduces the wireless body area sensor network (WBASN) used for communication in wearable sensors. Additionally, we discuss potential challenges and development directions that could shape the future of intelligent wearables and propose effective solutions for their continued enhancement. This article offers valuable insights into the exciting potential of intelligent wearables to transform healthcare and sports.

Flexible light-stimulated artificial synapse based on detached (In,Ga)N thin film for neuromorphic computing

Because of wide range of applications, the flexible artificial synapse is an indispensable part for next-generation neural morphology computing. In this work, we demonstrate a flexible synaptic device based on a lift-off (In,Ga)N thin film successfully. The synaptic device can mimic the learning, forgetting, and relearning functions of biological synapses at both flat and bent states. Furthermore, the synaptic device can simulate the transition from short-term memory to long-term memory successfully under different bending conditions. With the high flexibility, the excitatory post-synaptic current of the bent device only shows a slight decrease, leading to the high stability. Based on the experimental conductance for long-term potentiation and depression, the simulated three-layer neural network can achieve a high recognition rate up to 90.2%, indicating that the system comprising of flexible synaptic devices could have a strong learning-memory capability. Therefore, this work has a great potential for the development of wearable intelligence devices and flexible neuromorphic systems.

Humidity /temperature/ pH multi-responsive fabric-based colorimetric sensor for detection and information encryption

Relative humidity (RH), temperature, and pH as important environmental parameters that are closely related to industry, agriculture, and people's life. Therefore, the development of a colorimetric sensor that can monitor the RH, temperature, and pH in real-time is highly desired. In this work, a fabric-based flexible colorimetric sensor is easily manufactured by screen printing the mixture of cresol red (CR), thymol blue (TB) and boric acid (H3BO3) on the fabric. Compared with the traditional single stimulus colorimetric sensor, this fabric device can respond to multiple stimulus factors including humidity, temperature, or pH. The simple coating treatment can endow the colorimetric sensor with fast response without compromising its flexibility. Exposed to different stimulus conditions, the colorimetric sensor has a unique color change effect, such as humidity-responsive pink-yellow, temperature-responsive yellow-pink, and pH-responsive pink-purple. The flexibility and versatility of the fabric sensors are showcased with the applications in temperature/humidity/pH detection, cultural relics protection and information encryption. We believe the colorimetric sensor would have potential in environmental monitoring, health care, encrypted information patterns, and intelligent wearables, etc.

Preparation and Properties of Graphene Flexible Membrane

Graphene material has excellent electrical, thermal and mechanical properties, and the flexible film prepared by it can be widely used in intelligent wearables, industrial temperature control, biomedicine, home heating and other fields. In this paper, graphene paste was used to prepare textile substrate and non-substrate graphene flexible film, and its tensile strength, elongation at break, resistivity, hardness, modulus and other parameters were tested. The results show that the graphene flexible film has smooth appearance, good electrical conductivity and good mechanical strength, and is a good choice for smart wear and industrial parts gasket applications.

Bionic vine-stems structure for enhancing electrochromic cyclic stability and mechanical stability of multiband spectral regulation fiber

Wearable electrochromic (EC) devices can adjust visible-infrared spectra demonstrate promising potential for applications in display, decoration, personal thermal management, and adaptive thermal camouflage. However, existing flat layer-layer EC devices are hindered by poor air permeability, limited flexibility, and integration difficulties with fabrics, particularly in the realm of intelligent wearables. The creation of highly efficient fiber-shaped infrared (IR) modulation devices with exceptional structural and performance stability represents a significant challenge. We draw inspiration from the arboreous vine-stems structure observed in Chinese wistaria, which effectively minimizes the risk of damage to their vine and stems during harsh weather conditions. We have successfully designed a wearable high efficient multiband spectral regulation fiber (MSRF) with a bioinspired vine-stems structure using carbon fiber and polyaniline (PANI) as the active layer. The planar working electrode (Au) with PANI deposited on its surface was spirally wound around the counter electrode (carbon fiber) and separated by a gel electrolyte (PC/LiClO4), with polyolefin serving as the encapsulation layer. The distribution of electrical potential and lithium ions in the MSRF were analyzed using finite element analysis, demonstrating exceptional electrochromic stability without the length limit of the fiber. Moreover, the MSRF exhibited exceptional IR emissivity (△ε = 0.528) within the 8–14 μm range, fast response time (2.44 s) at low applied potentials (−0.8 V and 0.8 V), robust long-term cycling stability (at least 5000 cycles), and remarkable mechanical stability (at least 5000 bending cycles). The compatibility of the fiber with fabric and potential for patterning using MSRF with high length was also explored, providing new insights into the development of wearable adaptive thermal camouflage and spectral regulation fiber devices.

Mordo2: A Personalization Framework for Silent Command Recognition.

Wearable human-computer interactions in daily life are increasingly encouraged by the prevalence of intelligent wearables. It poses a demanding requirement of micro-interaction and minimizing social awkwardness. Our previous work demonstrated the feasibility of recognizing silent commands through around-ear biosensors with the limitation of user adaptation. In this work, we ease the limitation by a personalization framework that integrates spectral factorization of signals, temporal confidence rejection and commonly used transfer learning algorithms. Specifically, we first empirically formulate the user adaptation issue by presenting the accuracies of applying transfer learning algorithms to our previous method. Second, we improve the signal-to-noise ratio by proposing the supervised spectral factorization method that learns the amplitude and phase mappings between around-ear signals and the signals of articulated facial muscles. Third, we leverage the time continuity of commands and introduce the time decay into confidence rejection. Finally, extensive experiments have been conducted to evaluate the feasibility and improvements. The results indicate an average accuracy of 92.38% which is significantly larger than solely using transfer learning algorithms. And a comparable accuracy can be achieved with significantly reduced data of new users. The overall performance shows the framework can significantly improve the accuracy of user adaptations. The work would aid a further step toward commercial products for silent command recognition and inspire the solution to the user adaptation challenge of wearable human-computer interactions.

Research on High-Performance Fourier Transform Algorithms Based on the NPU

Backpack computers require powerful, intelligent computing capabilities for field wearables while taking energy consumption into careful consideration. A recommended solution for this demand is the CPU + NPU-based SoC. In many wearable intelligence applications, the Fourier Transform is an essential, computationally intensive preprocessing task. However, due to the unique structure of the NPU, the conventional Fourier Transform algorithms cannot be applied directly to it. This paper proposes two NPU-accelerated Fourier Transform algorithms that leverage the unique hardware structure of the NPU and provides three implementations of those algorithms, namely MM-2DFT, MV-2FFTm, and MV-2FFTv. Then, we benchmarked the speed and energy efficiency of our algorithms for the gray image edge filtering task on the Huawei Atlas200I-DK-A2 development kits against the Cooley-Tukey algorithm running on CPU and GPU platforms. The experiment results reveal MM-2DFT outperforms OpenCL-based FFT on NVIDIA Tegra X2 GPU for small input sizes, with a 4- to 8-time speedup. As the input image resolution exceeds 2048, MV-2FFTv approaches GPU computation speed. Additionally, two scenarios were tested and analyzed for energy efficiency, revealing that cube units of the NPU are more energy efficient. The vector and CPU units are better suited for sparse matrix multiplication and small-scale inputs, respectively.

Green One-Step Strategy of Conductive Ink for Active Health Monitoring in Rehabilitation and Early Care.

Conductive ink deposited on flexible substrates through simple methods such as dyeing or printing is one of the most promising approaches for scalable fabrication of wearable electronics. However, excessive chemical additives or a complex preparation process has limited the practical applications of conductive inks. Herein, a highly stable and antibacterial AgNPs/CNT/rGO (SACR) conductive ink with the only assistance of sustainable silk sericin (SS) is developed through a green one-step strategy. SS functions as not only the reductant of silver ions and GO by donating electrons but also the dispersant and stabilizer of CNTs through strong noncovalent interactions. The universality of SACR ink is demonstrated by depositing on various flexible substrates through handwriting, screen-printing, and dyeing techniques; meanwhile, the mechanical reliability between SACR ink and substrates is validated by peeling, bending, and twisting measurements. In addition, the synergistic effects of the multilevel hierarchical 0D/1D/2D structure and abundant interfacial interactions in SACR ink are advantageous to enhancing sensing performance. An SACR ink-based strain sensor and hydrogen peroxide (H2O2) sensor are fabricated to detect physical and biochemical indicators, demonstrating the enormous potential of SACR ink in intelligent wearables for active health monitoring in early care.

A Softwarized Intrusion Detection System for IoT-Enabled Smart Healthcare System

The Internet of Things-enabled Smart Healthcare System (IoT-SHS) is a networked infrastructure of intelligent wearables, software applications, health systems, and services that continuously monitors and transmits patient-sensitive data using an open wireless channel. The conventional security mechanisms are unsuitable for detecting attacks in the dynamic IoT-SHS context due to resource limitations and heterogeneity in low-cost healthcare devices. Deep Learning (DL) solutions for Intrusion Detection System (IDS) and softwarization of the network has the potential to achieve secure network services in the IoT-SHS environment. Motivated by the aforementioned discussion, we propose an intelligent softwarized IDS for protecting the critical infrastructure of the IoT-SHS ecosystem. Specifically, the DL-based IDS is designed using a hybrid cuda Long Short-Term Memory Deep Neural Network (cuLSTM-DNN) algorithm to assist network administrators in efficient decision-making for the generated intrusions. To further bolster the system’s resilience, we suggest a deployment architecture for the proposed CUDA-powered IDS using OpenStack Tacker in a real SDN environment, ensuring that virtual machines can directly utilize the host’s NVIDIA GPU, thereby streamlining and enhancing the network’s operational efficiency. The experimental results using the CICDDoS2019 dataset confirm the effectiveness of the proposed framework over some baseline and recent state-of-the-art techniques.

A Review of Manufacturing Methods for Flexible Devices and Energy Storage Devices.

Given the advancements in modern living standards and technological development, conventional smart devices have proven inadequate in meeting the demands for a high-quality lifestyle. Therefore, a revolution is necessary to overcome this impasse and facilitate the emergence of flexible electronics. Specifically, there is a growing focus on health detection, necessitating advanced flexible preparation technology for biosensor-based smart wearable devices. Nowadays, numerous flexible products are available on the market, such as electronic devices with flexible connections, bendable LED light arrays, and flexible radio frequency electronic tags for storing information. The manufacturing process of these devices is relatively straightforward, and their integration is uncomplicated. However, their functionality remains limited. Further research is necessary for the development of more intricate applications, such as intelligent wearables and energy storage systems. Taking smart wear as an example, it is worth noting that the current mainstream products on the market primarily consist of bracelet-type health testing equipment. They exhibit limited flexibility and can only be worn on the wrist for measurement purposes, which greatly limits their application diversity. Flexible energy storage and flexible display also face the same problem, so there is still a lot of room for development in the field of flexible electronics manufacturing. In this review, we provide a brief overview of the developmental history of flexible devices, systematically summarizing representative preparation methods and typical applications, identifying challenges, proposing solutions, and offering prospects for future development.

Intelligent wearable and portable security detection technologies integrated into protective equipment: A review

Intelligent wearable devices for security detection integrate flexible electronic components into protective equipment, enabling the detection of metals, explosives, human physiological conditions, and hazardous chemicals. While numerous studies have investigated various security detection technologies, a systematic literature review providing a comprehensive overview of the preparation and applications of intelligent wearables for security detection is lacking. Therefore, this paper aims to fill this gap by conducting a comprehensive examination of the application of intelligent wearables for security detection in specialized and temporary disposal environments. Additionally, the paper addresses existing challenges and discusses future directions for achieving greater progress in this domain.

Two new high-temperature molecular ferroelectrics [1,5-3.2.2-Hdabcn]X (X = ClO4−, ReO4−)

Molecular ferroelectrics have attracted much attention because of their excellent piezoelectricity, mechanical workability, and second harmonic effect. Here, we successfully prepared two molecular ferroelectrics [1,5–3.2.2-Hdabcn]X (X = ClO4−, 1; ReO4−, 2) by reactions of a quasi-spherical amine 1,5-diazabicycle[3.2.2]nonane (1.5–3.2.2-dabcn) with HX aqueous solution. Compounds 1 and 2 undergo high-temperature phase transitions at 381 K (1) and 396 K (2). Before and after the phase transition, they crystallize in the polar point group mm2, and the centrosymmetric point groups mmm and 4/mmm, respectively. According to Aizu rules, these two compounds experience mmmFmm2 and 4/mmmFmm2 type ferroelectric phase transitions, respectively. The ferroelectricity of both compounds is well expressed in their polycrystalline film at room temperature with low coercive voltages of 13 V for 1 and 25 V for 2. Using piezoelectric force microscopy (PFM), the 180° anti-parallel ferroelectric domains and the reversible polarization switching can be clearly observed in 1 and 2. This high-temperature molecular ferroelectric material has great application potential in flexible materials, biomechanics, intelligent wearables and other fields.

Self-Powered Photodetector Fabricated from a Single-Charge-Transfer Complex Nanowire Grown In Situ between Prefabricated Electrodes on an Si3N4 Membrane

Power consumption in an electronic circuit is one of the serious challenges that need to be improved to achieve a durable future. A photodetector is one such electronic device that consumes a huge external power to operate. This motivated the researchers to concentrate on self-powered optical photodetectors that can operate without external bias. On the contrary, building a device on a transparent film or nanomembrane has great importance in the field of electronic skins, and lightweight and intelligent wearables technology. Here, we have reported the fabrication and characterization of a self-power optical photodetector device based on a single Cu:7,7,8,8-tetracyanoquinodimethane nanowire (Cu:TCNQ NW) of length ∼500 nm and diameter ∼50 nm. The NW photodetector device was fabricated on a silicon nitride (Si3N4) membrane window (size = 100 μm × 100 μm, membrane thickness ∼100 nm) to meet the demands of a lightweight and transparent technology. The reported self-powered single-NW photodetector exhibits excellent photoresponsivity (∼5.5 A/W), high detectivity (∼ 7 × 107 Jones), outstanding external quantum efficiency (∼1.6 × 103%), and a large on/off current ratio (∼1.5 × 102) at 49 nW optical power. The analysis reveals that the contribution is due to photocarrier generation, radial built-in-field on the NW’s surface, and barrier height reduction during illumination. Self-powered optical photodetectors, in particular, have enormous potential as novel emerging self-driven optoelectronic devices.

Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors

Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time series modeling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and nonideal recording conditions. The experiments are conducted using a dataset of multisensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel two-dimensional convolutions for intrasensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time series models revealed that feedforward dilated convolutions with residual and skip connections outperformed all recurrent neural network (RNN)-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall, the results provide tangible suggestions on how to optimize end-to-end neural network training for multichannel movement sensor data.

Ultra-stretchable hydrogel thermocouples for intelligent wearables

Stretchable temperature sensors are necessary to enable tactile interaction and thermoregulation in the human body and soft robots. These sensors should be conformably adhered to a deformable surface and maintain temperature perception accuracy when stretched. However, current mainstream stretchable temperature sensors based on thermistors suffer from inherently unstable sensing during stretching due to the mutual interference of resistance changes caused by temperature and mechanical deformations. Herein, we propose an ultra-stretchable hydrogel thermocouple that provides unaltered temperature sensing upon stretching. The ultra-stretchability of this thermocouple is achieved by constructing thermogalvanic hydrogels with dynamic crosslinked double networks. By connecting P-type and N-type thermogalvanic hydrogels, the thermocouple exhibits a high equivalent See-beck coefficient of 1.93 mV K−1 and a stable sensitivity even under a 100% tensile strain. The advantage of this ultra-stretchable thermocouple is demonstrated in a smart glove prototype, which enables haptic feedback. Our work provides a new strategy for stretchable temperature sensors and may promote the development of intelligent wearables.

Hydrogels Enable Future Smart Batteries.

The growing trend of intelligent devices ranging from wearables and soft robots to artificial intelligence has set a high demand for smart batteries. Hydrogels provide opportunities for smart batteries to self-adjust their functions according to the operation conditions. Despite the progress in hydrogel-based smart batteries, a gap remains between the designable functions of diverse hydrogels and the expected performance of batteries. In this Perspective, we first briefly introduce the fundamentals of hydrogels, including formation, structure, and characteristics of the internal water and ions. Batteries that operate under unusual mechanical and temperature conditions enabled by hydrogels are highlighted. Challenges and opportunities for further development of hydrogels are outlined to propose future research in smart batteries toward all-climate power sources and intelligent wearables.