Intelligent Wearable Devices Research Articles

Intelligent assistive obstacle avoidance device based on SLAM and wearable technology

Abstract. This paper presents a conceptual design for an intelligent assistive device that combines visual simultaneous localisation and mapping (SLAM) with wearable technology. The device has been developed through the integration of two innovative fields: visual SLAM and wearable technology. The objective is to develop a device that is both user-friendly and safe. The design addresses the issue of safety for visually impaired individuals when they are outside the home. The objective is to provide a dependable, real-time, and resilient solution that can be utilised in intricate indoor and outdoor settings. The system is designed to provide reliable, real-time, and highly effective solutions in variable environments. The main components of the proposed system include visual SLAM, intelligent wearable devices (as carriers), and a comfortable and straightforward user feedback system (through haptic, auditory, or visual signals to provide feedback to the wearer), while simultaneously considering the safety and comfort of the device. This is due to the consideration of the prolonged and frequent periods of use by visually impaired individuals. This paper considers the latest advances in SLAM algorithms, improvements in wearable sensors and the latest developments in robotics for assisting visually impaired people. It also discusses the potential of these technologies in the future development of assistive devices. The aim is to provide a feasible, comfortable and safe solution that will enhance the safety and autonomy of visually impaired people when they are outside.

Analysis of electrode locations on limb condition effect for myoelectric pattern recognition

BackgroundGesture recognition using surface electromyography (sEMG) has garnered significant attention due to its potential for intuitive and natural control in wearable human–machine interfaces. However, ensuring robustness remains essential and is currently the primary challenge for practical applications.MethodsThis study investigates the impact of limb conditions and analyzes the influence of electrode placement. Both static and dynamic limb conditions were examined using electrodes positioned on the wrist, elbow, and the midpoint between them. Initially, we compared classification performance across various training conditions at these three electrode locations. Subsequently, a feature space analysis was conducted to quantify the effects of limb conditions. Finally, strategies for group training and feature selection were explored to mitigate these effects.ResultsThe results indicate that with the state-of-the-art method, classification performance at the wrist was comparable to that at the middle position, both of which outperformed the elbow, consistent with the findings from the feature space analysis. In inter-condition classification, training under dynamic limb conditions yielded better results than training under static conditions, especially at the positions covered by dynamic training. Additionally, fast and slow movement speeds produced similar performance outcomes. To mitigate the effects of limb conditions, adding more training conditions reduced classification errors; however, this reduction plateaued after four conditions, resulting in classification errors of 22.72%, 22.65%, and 26.58% for the wrist, middle, and elbow, respectively. Feature selection further improved classification performance, reducing errors to 19.98%, 19.75%, and 27.14% at the respective electrode locations, using three optimal features derived from single-condition training.ConclusionsThe study demonstrated that the impact of limb conditions was mitigated when electrodes were placed near the wrist. Dynamic limb condition training, combined with feature optimization, proved to be an effective strategy for reducing this effect. This work contributes to enhancing the robustness of myoelectric-controlled interfaces, thereby advancing the development of wearable intelligent devices.

Energy harvesting and human motion sensing of a 2D piezoelectric hybrid organic–inorganic perovskite

Two-dimensional (2D) hybrid organic–inorganic perovskites (HOIPs) have garnered plentiful attention as a result of their exceptional structural flexibility and multiple applications. In this study, we present a 2D HOIP (CHA)2PbBr4 (CHA = cyclohexylamine), for its potential applications in piezoelectricity, such as strain energy sensing and harvesting. Flexible composite films of (CHA)2PbBr4/PDMS (PDMS = polydimethylsiloxane) with a sequence of weight ratios (0, 5, 10, 15, 20 wt %) of (CHA)2PbBr4 were fabricated to analyze the energy harvesting properties. Experimental results demonstrated that a device with 15% composition exhibited the most optimized performance in energy harvesting, producing a peak magnitude of output voltage of 11.6 V, a short-circuit current of 0.38 µA, and a power density of 6.77 µW/cm2, along with notable stability exceeding 4000 cycles. Furthermore, this device exhibited high sensitivity in monitoring many varieties of human movements, such as finger bending and tapping, elbow bending, and gentle foot stamping. The findings of this study indicate that this 2D HOIP holds significant potential for use in flexible sensing and intelligent wearable devices.

Highly sensitive flexible pressure sensor based on laser ablated Ag-MWCNT /MXene for human motion detection

The complex and dynamic human environment, higher requirements are put forward for the high-performance flexible pressure sensors. The materials selection and microstructure design are crucial to achieve high sensitivity and stability. In this work, we fabricated pressure sensitive layer with multiple dimensional materials. Using laser ablation in liquid method, Ag nanoparticles were uniformly distributed in surface of multi-walled carbon nanotubes (MWCNT). Combined with the delaminated MXene, highly pressure sensitive composite could be prepared due to the reduction of contact resistance under pressure. The corresponding piezoresistive sensors showed high sensitivity (12.7 kPa−1), excellent response speed (150 ms), recovery speed (100 ms), and distinguished cycle stability (3000 cycles). Based on the performance of the sensor, it can realize the monitoring of tiny pressure on the human body, and further realize the detection of human activities and health. In addition, the sensor array has spatial recognition capability, providing a way of thinking for the development needs of future intelligent wearable electronic devices. The sensors can measure changes in resistance caused by finger bending, slight breathing, pulse, vocal cord vibration, and other movements to realize human activity and health monitoring. At the same time, the 3×3 sensor array system can recognize the layout of pressure in space. This simple preparation and low cost of the sensor can Facilitate the progress towards sophisticated wearable electronic gadgets.

Flexible ZIF-67@PVDF tree-like nanofiber membrane with high piezoelectric output for energy harvesting

Modern demands for efficient, environmentally friendly, and self-powered devices have led to growing interest in piezoelectric polymers in the scientific community. In this paper, a ZIF-67 functionalized polyvinylidene fluoride/ tetrabutylammonium hexafluorophosphate tree-like nanofiber membrane (ZIF-67@PVDF/TBAHP-TLNMs) was developed as piezoelectric nanogenerator for energy harvesting. The ZIF-67@PVDF/TBAHP-TLNMs was prepared by in-situ growth of ZIF-67 on the surface of PVDF TLNMs, which demonstrated remarkable energy harvesting capabilities. The results indicated that the β-phase content of the in-situ growth of ZIF-67 reached 86.94 %, augmented the fiber's rotation radius, and elevated the electromechanical conversion efficiency. Notably, the short circuit current reached 3.87 μA and the output voltage exceeded 9.78 V, which was 10.7 times and 5.9 times higher than the electrical signal output of pure PVDF nanofiber membrane, respectively. Furthermore, the ZIF-67@PVDF/TBAHP-TLNMs exhibited exceptional mechanical properties, with an elastic modulus as high as 1.13 GPa, outperforming other nanofiber membranes and catering to the diverse requirements of wearable devices. During a 500 s test cycle, its layered structure remained intact, without any discernible damage or interlayer separation of the nanofibers. At the same time, the device exhibited high electromechanical conversion efficiency and a sensitive sensing function, which made it have broad application prospects in human motion monitoring and intelligent wearable devices.

Fully integrated microneedle biosensor array for wearable multiplexed fitness biomarkers monitoring

Fitness monitoring has become increasingly important in modern lifestyles; the current fitness monitoring always relies on physical sensors, making it challenging to detect pertinent issues at a deeper level when exercising. Here, we report a fully integrated wearable microneedle sensor that simultaneously measures fitness related biomarkers (e.g., glucose, lactate, and alcohol) during physical exercise. Such a sensor integrates a biocompatible 3D-printed microneedle array that can comfortably access skin interstitial fluid and a small circuit for signal processing and calibration, and wireless communication. The microneedle array features good biocompatibility and highly sensitive biochemical sensors that can detect even the slightest variations within the biomarkers of this fluid. On-body experimental results indicate that such a sensor can monitor fitness-related biomarkers across multiple subjects and support multi-day monitoring, with results showing a good correlation with commercial devices. The data was transmitted to a smartphone via Bluetooth and uploaded to cloud platforms for further health assessment. This study has the potential to boost intelligent wearable devices in sports health.

Ultra-fast light repair, ultrasensitive, large strain detection range PDA@RGO/EVA composites fiber flexible strain sensor

The sensor fabricated from high-molecular polymer is soft, lightweight, and biocompatible; however, traditional high-molecular polymers suffer from viscoelasticity and poor cycle stability. To address these issues, it is essential to develop flexible matrix materials that can overcome viscoelasticity, ensuring high sensitivity and long-term use under large strains for next-generation wearable intelligent devices. In this study, ethylene-vinyl acetate (EVA) with a shape memory effect was utilized as the substrate for a flexible sensor，the shape memory effect of EVA can eliminate the residual strain generated by each long time of use to improve its service life. A polydopamine@reduced graphene oxide (PDA@RGO)/EVA fiber flexible strain sensor was fabricated through melt extrusion, swelling ultrasound, and in-situ polymerization techniques. The resulting sensor exhibits high sensitivity (gauge factor=1801.21), a wide strain detection range (strain = 193 %), and excellent stability and durability (2000 cycles). Notably, the PDA@RGO/EVA fiber flexible strain sensor demonstrates exceptional dual thermal and light repair functions, with light repair achieving a repair speed of 5 seconds, 100 % electrical performance repair efficiency, and perfect consistency in relative resistance response before and after repair. Furthermore, the PDA@RGO/EVA strain sensor can monitor finger and wrist movements in real-time with high accuracy, highlighting its significant potential application in the wearable technology field.

A super-elastic wearable strain sensor based on PU/CNTs yarns for human-motion detection

Yarn-based strain sensors increasingly garner attention for their potential applications in intelligent wearable electronic devices. Developing highly elastic conductive composite yarns and integrating them into fabrics poses a significant challenge in manufacturing flexible electronic devices. In this study, a polyurethane (PU)/carbon nanotubes (CNTs) composite nanofiber yarn was successfully and continuously prepared using a simple conjugate spinning technique. The resulting yarn exhibited excellent mechanical properties, with a high tensile strength of up to 56.7 MPa and an elongation at break of 504 %. A yarn with a dual conductive network was created using CNTs ink dip-coating. It demonstrated excellent conductivity (8.77 S/cm), exceptional linear variation, and reusability. The sensor remained stable after 1000 cycles of testing, exhibiting a relative electrical resistance change of up to 594 % under a strain of 350 %. Additionally, taking advantage of the inherent knittability of one-dimensional yarn structures, a susceptible wearable textile-type strain sensor was successfully developed using textile knitting technology. This sensor can be directly attached to the surface of the human body for real-time monitoring of human body dynamics. This sensor showcases significant promise for use in flexible, intelligent, wearable electronic devices.

Phase change hydrogels with tunable adhesion for wearable thermal management and intelligent healthcare

Intelligent wearable devices integrating real-time health monitoring and on-demand treatment are obtaining widespread attention. However, precise monitoring and prolonged photothermal therapy of human bodies still face significant challenges. Herein, a phase change gel (PCG) with simultaneous strain sensing and photothermal therapy functions was fabricated using sodium sulfate decahydrate (SSD, Na2SO4·10H2O), polyacrylamide (PAM), and polydopamine-modified MXene (PDA@MXene). The PCGs possessed a suitable phase transition temperature and high enthalpy value (128.8 J/g), which provided sufficient thermal comfort. The PAM hydrogel effectively overcame the liquid leakage of SSD and achieved flexibility. PDA@MXene not only imparted the PCGs with excellent photothermal conversion efficiency (93.83 %), but also ensured strong adhesive properties to precisely monitor human motions. Interestingly, the PCGs exhibited temperature-sensitive adhesion ability by triggering the phase transition of SSD, thus obtaining high adhesion to the skin and painless detachment simultaneously. The temperature-induced adhesive PCGs exhibit great application prospects in portable medicine devices.

High optical contrast, dual-responsive, wearable photochromic fibers for smart textile engineering

Smart textiles exhibiting responsiveness to external light stimuli show a great scope of applications in personalized decorations and intelligent wearable devices, among others. It's both rewarding and challenging to build wearable indicators based on fibers with high contrast and versatility to monitor the state of the external environment while maintaining good conformability and breathability for practical use. Here, we have integrated spiropyran photochromic compound (SP) into the spinning solution with polyvinyl alcohol (PVA) and aqueous polyurethane (WPU), and developed fibers by wet spinning featuring photochromic and photofluorescent dual outputs. Under UV and visible light illumination, the fibers demonstrate significant contrast optical signals, rapid color switching (4 s), long retention duration (7.5 h), as well as good cycling stability (20 times). The photochromic fibers can be used in the form of photochromic wristbands or embroidered on various textiles as flexible wearable UV indicators. Furthermore, the UV-induced fibers also produce pertinent color changes when subjected to acidic and alkaline vapors. We envision that the as-prepared fibers present promising applications in environmental acidity detection and multi-stimulus responsive materials.

Design of flexible micro-porous fiber with double conductive network synergy for high-performance strain sensor

Flexible wearable strain sensor is a vital part of intelligent wearable systems due to their broad applications in human–machine interaction and smart clothes. Flexible strain sensor fabricated via conventional blending conductive polymer composites has some drawbacks, for example, low sensitivity, large hysteresis, and “shoulder-peak” phenomenon, which limit their practical applications greatly. Herein, we prepared a fiber-shaped strain sensor with a micro-porous, three-layer structure together with a dual conductive network based on reduced graphene oxide (rGO) and multi-walled carbon nanotubes (MWCNTs). The strain sensor demonstrates excellent response behaviors, including splendid stretchability (686 %), wide detection range (120 %), high sensitivity (gauge factor = 300), fast response time (300 ms) and excellent durability (over 9000 cycles). The unique dual conductive network structure endows the strain sensor with remarkable recoverability and reproducibility. The resistance of the strain sensor can recover to nearly initial conditions after the releasing process, exhibiting lower hysteresis. The “shoulder-peak” phenomenon has also been weakened based on the microstructure and conductive network design. Based on the high sensitivity and excellent response behavior, the fiber-shaped strain sensor performs real-time monitoring of breathing, finger bending, walking, and other human motions, providing a good candidate for its application in intelligent wearable devices.

Deep learning-assisted flexible piezoresistive sensor with liquid-phase reduced metal electrodes for fitness movement recognition and correction

Flexibility and wearability in electronic devices gain prominence with the rise of national fitness campaigns. Among them, piezoresistive sensors stand out for their ability to accurately monitor health signals due to their high sensitivity. However, conventional metal nanoparticle ink electrodes face issues like peeling, chemical instability, and substrate limitations. This study introduces a novel method for fabricating high-performance flexible piezoresistive sensors using liquid-phase reduced metal electrodes. Integration of porous polydimethylsiloxane (PDMS) substrates with highly conductive interdigital silver electrodes (1.6 × 10−6 Ω·m) addresses conventional electrode shortcomings, offering permeability, flexibility, and outstanding conductivity. Incorporation of a graphene (GR)/carbon nanotube (CNT)/Ecoflex composite enhances sensor piezoresistive sensing capabilities, with features including high sensitivity (3.57 KPa−1), rapid response time (58 ms/72 ms), and excellent cycle stability (>10000 cycles). The sensor finds utility in various applications, including human physiological signal monitoring, pressure array recognition, and handwriting recognition. Additionally, with deep learning techniques, the system achieves accurate recognition (99.25 %) and correction (98.75 %) of diverse fitness movements, aiming to promote safer exercise practices, enhance training efficiency, and advance intelligent wearable fitness devices.

A Review on Blockchain Technology based Secure Intelligent Wearable Devices for 6G Systems

A Review on Blockchain Technology based Secure Intelligent Wearable Devices for 6G Systems

Wearable glove gesture recognition based on fiber Bragg grating sensing using genetic algorithm-back propagation neural network

The recognition of gestures through intelligent wearable devices has a broad array of applications, including low-visibility industrial sites and robotics. However, accurately and swiftly recognizing gestures poses a significant challenge. This study addresses the complex interplay between multimodal information sensing and signal processing with flexible sensors in wearable data gloves by conducting theoretical and experimental investigations into gesture recognition using fiber Bragg grating (FBG) flexible sensors. First, the degree of bending and bending angle of the FBG sensor were calibrated, and the impact of temperature was investigated. Subsequently, a human hand wearable data glove gesture recognition system based on fiber grating sensing was constructed, and gesture recognition experiments were conducted. Finally, an enhanced error backpropagation (BP) neural network model for gesture recognition based on genetic algorithms (GA) was established and tested and the results were compared with those achieved using a conventional error BP neural network. The comparison revealed that under the same network structure, the recognition accuracy of the GA-BP neural network of approximately 100% was higher than the conventional BP neural network’s accuracy of 95.77%. Thus, the development of gesture recognition using FBG sensors provides a theoretical foundation and technical support for multiple applications of wearable data gloves, driving their adoption in areas such as human–computer interactions, intelligent robotics, and complex high-risk environments.

Biodegradable flexible triboelectric nanogenerator for winter sports monitoring

With the energy crisis and environmental pollution becoming a growing concern worldwide, the development of clean and renewable energy from the environment has become an imperative for human survival and development. However, the equipment used to harvest clean renewable energy is large, subject to environmental impacts and regional differences (such as wind, solar and tidal energy). In this study, a biodegradable eggshell membrane triboelectric nanogenerator (EM-TENG) is introduced for the purpose of harvesting low-frequency mechanical energy. A Wireless Intelligent Motion Monitoring System (WIMMS) has been created using EM-TENG. It includes a Bluetooth sensor terminal and an intelligent processing terminal for digital signal reception on a host computer. The EM-TENG can be attached to knee and ankle joints to monitor posture. Therefore, for real-time monitoring of joint and kinetic chain changes during land training of ice dance athletes, the intelligent ice dance land training aid system is important. As a wearable motion monitoring sensor, EM-TENGs application in intelligent motion monitoring, intelligent wearable devices and big data analytics is being promoted.

Exploring the Application of Force Intelligent Wearable Devices in Quantitative Management of Daily Physical Activity

This study looks into how force-intelligent wearable devices can be used to quantitatively manage daily physical activity levels. The study intends to investigate the effectiveness of these devices in measuring various physical activity metrics such as step count, distance walked, calories burned, and exercise intensity. A thorough methodology is used, which includes device selection, participant recruiting, data collecting, and analysis methods. Participants wear the selected wearable devices continuously for a set amount of time, enabling longitudinal tracking of daily physical activity patterns. The acquired data is evaluated using statistical analysis approaches such as descriptive statistics, correlation analysis, and cross-demographic group comparisons. The findings provide insights into participants' activity levels, device accuracy, user engagement, and relationships between various physical activity measures. The study's findings help to better understand the usefulness of force-intelligent wearable devices in promoting physical activity and improving health outcomes. Further research in this area could help to inform programs aimed at increasing physical activity and general well-being.

An examination of wearable technology in the field biomedical engineering: A review

A wearable device is a piece of technology that can be worn on the body, designed to be portable, lightweight and equipped with various sensors or features for specific functions. As a game-changer in the field of biomedical engineering, wearable technology provides innovative approaches to illness management, individualized healthcare and health monitoring. This review looks at wearable technology as it stands in the field of biomedical engineering, emphasizing its uses, difficulties and potential. The research examines a wide variety of wearable wireless biosensors, sensor patches, wristwatches, wearable face masks and multi-sensor smart clothing, emphasizing their potential to measure physiological parameters, including blood pressure, heart rate and sleep patterns. This study demonstrates the need for intelligent wearable devices for high-quality signal capture, processing and continuous monitoring of critical parameters. The creation of smart wearables that meet the specified design standards enables the creator of a wearable with reduced power consumption that can be used to monitor the health of patients. This study offers a thorough analysis of wearable technology's present state, obstacles and potential applications in the field of biomedical engineering. It will be of great use to investigators, medical professionals and developers of technology.

The Current Situation, Characteristics and Prospects of the Research on Tennis Theme Literature in China from the Perspective of Artificial Intelligence

With the rapid development of artificial intelligence technology, the function of artificial intelligence to help the high-quality development of sports is increasingly visible, and the continuous, comprehensive and deep integration of artificial intelligence and sports will be an inevitable trend. This paper summarized and analyzed the characteristics of the tennis research theme literature published in the core journals of China from 2012 to 2022 by using the methods of literature and logical induction. The results show that, in the past 10 years, there are problems such as insufficient use of artificial intelligence in technical and tactical statistical analysis, insufficient use in tennis professionalization research, lack of artificial intelligence perspectives in tennis tournament research, and shortage of artificial intelligence use in tennis psychology research. In the future, scientific research on tennis in China should rely on scientific and technological means such as 5G technology, big data technology, computer vision and intelligent wearable devices. etc, cross integrate multidisciplinary knowledge, accurately locate the physical fitness weaknesses of Chinese tennis players, focus on building an intelligent measurement system for tennis players, and scientifically develop a big data platform for tennis skills and tactics; And guided by practical problems, we should focus on exploring intelligent wearable devices suitable for tennis sports programs, and integrating computer vision technology with intelligent wearable devices to achieve complementary advantages and enhance the accuracy and credibility of data collection. And use natural science methods to conduct quantitative analysis to provide scientific solutions for the development of tennis.

Nacre-Mimetic Structure Multifunctional Ion-Conductive Hydrogel Strain Sensors with Ultrastretchability, High Sensitivity, and Excellent Adhesive Properties.

Recently, conductive hydrogels have emerged as promising materials for smart, wearable devices. However, limited mechanical properties and low sensitivity greatly restrict their lifespan. Based on the design of biomimetic-layered structure, the conductive hydrogels with nacre-mimetic structure were prepared by using layered acrylic bentonite (AABT) and phytic acid (PA) as multifunctional "brick" and "mortar" units. Among them, the unique rigid cyclic multihydroxyl structure of the "organic mortar" PA preserves both ultrastretchability (4050.02%) and high stress (563.20 kPa) of the hydrogel, which far exceeds most of the reported articles. Because of the synergistic effect of AABT and PA, the hydrogel exhibits an excellent adhesive strength (87.74 kPa). The role of AABT in the adhesive properties of hydrogels is proposed for the first time, and a general strategy for improving the adhesive properties of hydrogels by using AABT is demonstrated. Furthermore, AABT provides ion channels and PA ionizes abundant H+, conferring a high gauge factor (GF = 14.95) and excellent antimicrobial properties to the hydrogel. Also, inspired by fruit batteries, simple self-powered flexible sensors were developed. Consequently, this study provides knowledge for functional bentonite filler modified hydrogel, and the prepared multifunctional ionic conductive hydrogel shows great application potential in the field of intelligent wearable devices.

Fully‐Recyclable Green and Fire‐Resistant Triboelectric Elastomer via Geometrical Synergistic Fillers and Its Applications for Smart Firefighting Boots

AbstractWearable sensors for high temperatures (WSHT) applications pose a significant challenge in the electronics field due to the inherent inability of most electronics to function in harsh environments. Triboelectric nanogenerators (TENG) present a substantial potential for applications in such challenging conditions. Herein, a fully‐recyclable flame‐retardant triboelectric elastomer is developed, which exhibits superior charge stability under high temperature conditions and proposes the specially designed synergistic fillers that play a dual role by trapping more charges into the interface and stabilizing the char layers via layer‐ball‐layer structure. An efficient flame‐retardant system is presented, which exhibits a 31% lower fire growth index than the individual components. The maximum surface charge density of this triboelectric elastomer can reach 361 µC m−2, while this surface charge density can be preserved to 70.9% after 16 s of combustion (≈520 °C) and to 37.9% by continuously working at 130 °C. At last, utilizing this flame‐retardant triboelectric elastomer, smart firemen's boots featuring gait analysis for identification and second level early warning systems have been developed. This work paves the way for the development of triboelectric materials and intelligent wearable devices suitable for applications in harsh environments.