Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications

Ingenious Structure Engineering to Enhance Piezoelectricity in Poly(vinylidene fluoride) for Biomedical Applications

The future development of wearable/implantable sensing and medical devices relies on substrates with excellent flexibility, stability, biocompatibility, and self-powered capabilities. Enhancing the energy efficiency and convenience is crucial, and converting external mechanical energy into electrical energy is a promising strategy for long-term advancement. Poly(vinylidene fluoride) (PVDF), known for its piezoelectricity, is an outstanding representative of an electroactive polymer. Ingeniously designed PVDF-based polymers have been fabricated as piezoelectric devices for various applications. Notably, the piezoelectric performance of PVDF-based platforms is determined by their structural characteristics at different scales. This Review highlights how researchers can strategically engineer structures on microscopic, mesoscopic, and macroscopic scales. We discuss advanced research on PVDF-based piezoelectric platforms with diverse structural designs in biomedical sensing, disease diagnosis, and treatment. Ultimately, we try to give perspectives for future development trends of PVDF-based piezoelectric platforms in biomedicine, providing valuable insights for further research.

Multimodal Intelligent Flooring System for Advanced Smart-Building Monitoring and Interactions

The floor constitutes one of the largest areas within a building with which users interact most frequently in daily activities. Employing floor sensors is vital for smart-building digital twins, wherein triboelectric nanogenerators demonstrate wide application potential due to their good performance and self-powering characteristics. However, their sensing stability, reliability, and multimodality require further enhancement to meet the rapidly evolving demands. Thus, this work introduces a multimodal intelligent flooring system, implementing a 4 × 4 floor array for multimodal information detection (including position, pressure, material, user identity, and activity) and human-machine interactions. The floor unit incorporates a hybrid structure of triboelectric pressure sensors and a top-surface material sensor, facilitating linear and enhanced sensitivity across a wide pressure range (0-800 N), alongside the material recognition capability. The floor array is implemented by an advanced output-ratio method with minimalist output channels, which is insensitive to environmental factors such as humidity and temperature. In addition to multimodal sensing, energy harvesting is co-designed with the pressure sensors for scavenging waste energy to power smart-building sensor nodes. This developed flooring system enables multimodal sensing, energy harvesting, and smart-sport interactions in smart buildings, greatly expanding the floor sensing scenarios and applications.

A Smart Ski Pole for Skiing Pattern Recognition and Quantification Application

In cross-country skiing, ski poles play a crucial role in technique, propulsion, and overall performance. The kinematic parameters of ski poles can provide valuable information about the skier's technique, which is of great significance for coaches and athletes seeking to improve their skiing performance. In this work, a new smart ski pole is proposed, which combines the uniaxial load cell and the inertial measurement unit (IMU), aiming to provide comprehensive data measurement functions more easily and to play an auxiliary role in training. The ski pole can collect data directly related to skiing technical actions, such as the skier's pole force, pole angle, inertia data, etc., and the system's design, based on wireless transmission, makes the system more convenient to provide comprehensive data acquisition functions, in order to achieve a more simple and efficient use experience. In this experiment, the characteristic data obtained from the ski poles during the Double Poling of three skiers were extracted and the sample t-test was conducted. The results showed that the three skiers had significant differences in pole force, pole angle, and pole time. Spearman correlation analysis was used to analyze the sports data of the people with good performance, and the results showed that the pole force and speed (r = 0.71) and pole support angle (r = 0.76) were significantly correlated. In addition, this study adopted the commonly used inertial sensor data for action recognition, combined with the load cell data as the input of the ski technical action recognition algorithm, and the recognition accuracy of five kinds of cross-country skiing technical actions (Diagonal Stride (DS), Double Poling (DP), Kick Double Poling (KDP), Two-stroke Glide (G2) and Five-stroke Glide (G5)) reached 99.5%, and the accuracy was significantly improved compared with similar recognition systems. Therefore, the equipment is expected to be a valuable training tool for coaches and athletes, helping them to better understand and improve their ski maneuver technique.

Environmentally Robust Triboelectric Tire Monitoring System for Self-Powered Driving Information Recognition via Hybrid Deep Learning in Time-Frequency Representation

Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire-road friction. The optimization of the process and structure of a laser-induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200-2000 rpm and contact fractions of line. Employing a hybrid model combining short-term Fourier transform with a convolution neural network-long short-term memory, the LIG-based triboelectric tire monitoring (LTTM) system decouples the driving information, such as traffic lines and road states, from varied environmental conditions of humidity (10%-90%) and temperatures (50-70 °C). The real-time line and road state recognition of the LTTM system is confirmed on a mobile platform across diverse environmental conditions, including fog, dampness, intense sunlight, and heat shimmer. This work provides an environmentally robust monitoring AIoT system by introducing a self-powered triboelectric sensor and hybrid deep learning for smart mobility.

A Flexible Capacitive Pressure Sensor with Adjustable Detection Range Based on the Inflatable Dielectric Layer for Human-Computer Interaction

As an essential component in wearable electronic devices and intelligent robots, flexible pressure sensors have enormous application value in fields such as healthcare, human-computer interaction, and intelligent perception. However, due to the complex and ever-changing pressure loads borne by sensors in different application scenarios, this also puts great demands on the flexible response and adjustment ability of a sensor's detection range. Therefore, developing a flexible pressure sensor with a wide and adjustable detection range, which can be applied flexibly under different pressure loads, is also a major challenge in current research. In this paper, we propose a flexible pressure sensor with a wide and adjustable detection range based on an inflatable adjustable safety airbag as the dielectric layer. This sensor uses inflatable airbags prepared using 3D printing technology and silicone reverse molding technology as the dielectric layer and achieves high sensitivity (0.6 kPa-1 to 1.19 kPa-1), wide detection range (220-1500 kPa), and flexible performance applicability by adjusting the air pressure inside the dielectric layer. At the same time, its simple production process, convenient production, fast response time (100 ms), and good stability provide the possibility for the flexible application of sensors in different pressure detection. The experimental results indicate that the sensor has enormous potential for applications in wearable devices, healthcare, human-computer interaction, and intelligent perception recognition.

Recent Advances in Self-Powered Wearable Flexible Sensors for Human Gaits Analysis

The rapid progress of flexible electronics has met the growing need for detecting human movement information in exoskeleton auxiliary equipment. This study provides a review of recent advancements in the design and fabrication of flexible electronics used for human motion detection. Firstly, a comprehensive introduction is provided on various self-powered wearable flexible sensors employed in detecting human movement information. Subsequently, the algorithms utilized to provide feedback on human movement are presented, followed by a thorough discussion of their methods and effectiveness. Finally, the review concludes with perspectives on the current challenges and opportunities in implementing self-powered wearable flexible sensors in exoskeleton technology.

A high-performance triboelectric nanogenerator with dual nanostructure for remote control of switching circuit

Preparing nanostructured surfaces has been considered an effective method to improve the output of triboelectric nanogenerators (TENGs), but how to quickly prepare materials with a nanostructured surface for TENGs has always been a challenge. Here, polypropylene nanowires and electrospun nylon 11 nanofibers were successfully prepared through a simple and time-saving method with a high success rate. Compared with a flat TENG, the output performance of a dual nanostructured TENG is enhanced by more than 5 times. After 1H,1H,2H,2H-perfluorooctyl trichlorosilane was assembled on the surface of the polypropylene film, the dual nanostructured TENG achieved the maximum output with the short-circuit current, output voltage, and charge density of 63.3 μA, 1135 V and 161.5 μC m-2, respectively. Compared with a planar structured TENG, the short-circuit current and output voltage were enhanced by about 18 times, and the charge density was increased by about 36 times. In addition, the TENG showed good working stability with almost no decrease in output after continuous operation for 193 000 cycles. The electricity generated by this TENG can successfully light up 1280 LEDs and continuously power a multi-functional electronic watch. Finally, the triboelectric signal generated by this TENG was used to control an optocoupler switch, indicating good application prospects in a remote control switching circuit.

Multistrand Twisted Triboelectric Kevlar Yarns for Harvesting High Impact Energy, Body Injury Location and Levels Evaluation

Developing ultrahigh-strength fabric-based triboelectric nanogenerators for harvesting high-impact energy and sensing biomechanical signals is still a great challenge. Here, the constraints are addressed by design of a multistrand twisted triboelectric Kevlar (MTTK) yarn using conductive and non-conductive Kevlar fibers. Manufactured using a multistrand twisting process, the MTTK yarn offers superior tensile strength (372 MPa), compared to current triboelectric yarns. In addition, a self-powered impact sensing fabric patch (SP-ISFP) comprising signal acquisition, processing, communication circuit, and MTTK yarns is integrated. The SP-ISFP features withstanding impact (4 GPa) and a sensitivity and response time under the high impact condition (59.68 V GPa-1; 0.4 s). Furthermore, a multi-channel smart bulletproof vest is developed by the array of 36 SP-ISFPs, enabling the reconstruction of impact mapping and assessment of body injury location and levels by real-time data acquisition. Their potential to reduce body injuries, professional security, and construct a multi-point personal vital signs dynamic monitoring platform holds great promise.

Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study

BACKGROUND

Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways.

OBJECTIVE

For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized.

METHODS

In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: -20%, -15%, -10%, -5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests.

RESULTS

Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking.

CONCLUSIONS

While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.

Frequency-encoded eye tracking smart contact lens for human-machine interaction

Eye tracking techniques enable high-efficient, natural, and effortless human-machine interaction by detecting users' eye movements and decoding their attention and intentions. Here, a miniature, imperceptible, and biocompatible smart contact lens is proposed for in situ eye tracking and wireless eye-machine interaction. Employing the frequency encoding strategy, the chip-free and battery-free lens successes in detecting eye movement and closure. Using a time-sequential eye tracking algorithm, the lens has a great angular accuracy of <0.5°, which is even less than the vision range of central fovea. Multiple eye-machine interaction applications, such as eye-drawing, Gluttonous Snake game, web interaction, pan-tilt-zoom camera control, and robot vehicle control, are demonstrated on the eye movement model and in vivo rabbit. Furthermore, comprehensive biocompatibility tests are implemented, demonstrating low cytotoxicity and low eye irritation. Thus, the contact lens is expected to enrich approaches of eye tracking techniques and promote the development of human-machine interaction technology.

High Sensitivity and Wide Linear Range Flexible Piezoresistive Pressure Sensor with Microspheres as Spacers for Pronunciation Recognition

Flexible piezoresistive pressure sensors have received great popularity in flexible electronics due to their simple structure and promising applications in health monitoring and artificial intelligence. However, the contradiction between sensitivity and detection range limits the application of the sensors in the medium-pressure regime. Here, a flexible piezoresistive pressure sensor is fabricated by combining a hierarchical spinous microstructure sensitive layer and a periodic microsphere array spacer. The sensor achieves high sensitivity (39.1 kPa-1) and outstanding linearity (0.99, R2 coefficient) in a medium-pressure regime, as well as a wide range of detection (100 Pa-160.0 kPa), high detection precision (<0.63‰ full scale), and excellent durability (>5000 cycles). The mechanism of the microsphere array spacer in improving sensitivity and detection range was revealed through finite element analysis. Furthermore, the sensors have been utilized to detect muscle and joint movements, spatial pressure distributions, and throat movements during pronouncing words. By means of a full-connect artificial neural network for machine learning, the sensor's output of different pronounced words can be precisely distinguished and classified with an overall accuracy of 96.0%. Overall, the high-performance flexible pressure sensor based on a microsphere array spacer has great potential in health monitoring, human-machine interface, and artificial intelligence of medium-pressure regime.

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.

AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Fabrication of Electrode Material for Textile-Based Triboelectric Nanogenerators: Research of the Relationship between Output Performance and Dielectric Material Strain

In this work, a textile-based triboelectric nanogenerator (TENG) device was developed through electroless plating technology to prepare electrode material. Hydrophilic groups on the fiber surface are able to absorb Ag+, which could play a role in the center of a catalyst to reduce Cu2+ to fabricate Cu-coated cotton toward the fabrication of TENG electrode material. The TENG device established admirable performance and good stabilization, and a maximum voltage at 9.6 V was detected when the stress and strain on the polydimethylsiloxane layer are 82.6 kPa and 5.8%, respectively. In addition, the relationships among device properties and strain/thickness of dielectric materials have been explored in depth as well. The output voltage of the device increases gradually with the enhancement of dielectric strain and stress. As expected, the TENG as-fabricated device was installed to various physical behaviors to illustrate the harvesting of power of knee-jerk movements.

Performance-Enhanced Flexible Self-Powered Tactile Sensor Arrays Based on Lotus Root-Derived Porous Carbon for Real-Time Human-Machine Interaction of the Robotic Snake

Flexible tactile sensors play an important role in the development of wearable electronics and human-machine interaction (HMI) systems. However, poor sensing abilities, an indispensable external energy supply, and limited material selection have significantly constrained their advancement. Herein, a self-powered flexible triboelectric sensor (TES) is proposed by integrating lotus-root-derived porous carbon (PC) into polydimethylsiloxane (PDMS). Owing to the superior charge capturing capability of PC, the PDMS/PC (PPC)-based TES exhibits an open-circuit voltage (Voc) of 22.8 V when it is periodically patted by skin at the pressure of 2 N and the frequency of 1 Hz, which is 5 times higher than that of a pristine PDMS-based TES. Furthermore, the as-prepared self-powered TES exhibits a high sensitivity of 3.24 V kPa-1 below 15 kPa for detecting human motion signals, such as finger clicks, joint bends, etc. Last but not the least, after the assembly of a PPC-based TES array and construction of an HMI system, the robotic snake can be controlled remotely by recognizing finger touching signals. This work shows broad potential applications for the self-powered TES in the fields of intelligent robotics, flexible electronics, disaster relief, and intelligence spying.

Triboelectric Nanogenerators for Preventive Health Monitoring

With the improvement in life quality, the increased focus on health has expedited the rapid development of portable preventative-health-monitoring devices. As one of the most attractive sensing technologies, triboelectric nanogenerators (TENGs) are playing a more and more important role in wearable electronics, machinery condition monitoring, and Internet of Things (IoT) sensors. TENGs possess many advantages, such as ease of fabrication, cost-effectiveness, flexibility, material-selection variety, and the ability to collect low-frequency motion, offering a novel way to achieve health monitoring for human beings in various aspects. In this short review, we initially present the working modes of TENGs based on their applications in health monitoring. Subsequently, the applications of TENG-based preventive health monitoring are demonstrated for different abnormal conditions of human beings, including fall-down detection, respiration monitoring, fatigue monitoring, and arterial pulse monitoring for cardiovascular disease. Finally, the discussion summarizes the current limitations and future perspectives. This short review encapsulates the latest and most influential works on preventive health monitoring utilizing the triboelectric effect for human beings and provides hints and evidence for future research trends.

Deep-learning enabled smart insole system aiming for multifunctional foot-healthcare applications

Real-time foot pressure monitoring using wearable smart systems, with comprehensive foot health monitoring and analysis, can enhance quality of life and prevent foot-related diseases. However, traditional smart insole solutions that rely on basic data analysis methods of manual feature extraction are limited to real-time plantar pressure mapping and gait analysis, failing to meet the diverse needs of users for comprehensive foot healthcare. To address this, we propose a deep learning-enabled smart insole system comprising a plantar pressure sensing insole, portable circuit board, deep learning and data analysis blocks, and software interface. The capacitive sensing insole can map both static and dynamic plantar pressure with a wide range over 500 kPa and excellent sensitivity. Statistical tools are used to analyze long-term foot pressure usage data, providing indicators for early prevention of foot diseases and key data labels for deep learning algorithms to uncover insights into the relationship between plantar pressure patterns and foot issues. Additionally, a segmentation method assisted deep learning model is implemented for exercise-fatigue recognition as a proof of concept, achieving a high classification accuracy of 95%. The system also demonstrates various foot healthcare applications, including daily activity statistics, exercise injury avoidance, and diabetic foot ulcer prevention.

Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing

Materials science research has garnered extensive attention from industry, society, policy, and academia. However, understanding the research landscape and extracting strategic insights are challenging due to the increasing diversity and volume of publications. This study proposes a natural language processing-based protocol for extracting text-encoded topics from a large volume of scientific literature, uncovering research interests of scientific communities, as well as convergence trends. We report a topic map, representing the materials science research landscape with text-mined 257 topics regarding biocompatible materials, structural materials, electrochemistry, or photonics. We analyze the topic map in terms of national research interests in materials science, revealing competitive positions and strategies of active nations. For example, it is found that the increasing trend of research interest in machine learning topic was captured in the United States earlier than other nations. Similarly, our journal-level analyses serve as reference information for journal recommendations and trend guidance, showing that the main topics and research interests of materials science journals slightly changed over time. Moreover, we build the topic association network which can highlight the status and future potential of interdisciplinary research, revealing research fields with high centrality in the network such as machine learning-enabled composite modeling, energy policy, or wearable electronics. This study offers insightful results on current and near-future materials science research landscapes, facilitating the understanding of stakeholders, amidst the fast-evolving and diverse knowledge of materials science.

The Role of AI in Serious Games and Gamification for Health: Scoping Review

BACKGROUND

Artificial intelligence (AI) and game-based methods such as serious games or gamification are both emerging technologies and methodologies in health care. The merging of the two could provide greater advantages, particularly in the field of therapeutic interventions in medicine.

OBJECTIVE

This scoping review sought to generate an overview of the currently existing literature on the connection of AI and game-based approaches in health care. The primary objectives were to cluster studies by disease and health topic addressed, level of care, and AI or games technology.

METHODS

For this scoping review, the databases PubMed, Scopus, IEEE Xplore, Cochrane Library, and PubPsych were comprehensively searched on February 2, 2022. Two independent authors conducted the screening process using Rayyan software (Rayyan Systems Inc). Only original studies published in English since 1992 were eligible for inclusion. The studies had to involve aspects of therapy or education in medicine and the use of AI in combination with game-based approaches. Each publication was coded for basic characteristics, including the population, intervention, comparison, and outcomes (PICO) criteria; the level of evidence; the disease and health issue; the level of care; the game variant; the AI technology; and the function type. Inductive coding was used to identify the patterns, themes, and categories in the data. Individual codings were analyzed and summarized narratively.

RESULTS

A total of 16 papers met all inclusion criteria. Most of the studies (10/16, 63%) were conducted in disease rehabilitation, tackling motion impairment (eg, after stroke or trauma). Another cluster of studies (3/16, 19%) was found in the detection and rehabilitation of cognitive impairment. Machine learning was the main AI technology applied and serious games the main game-based approach used. However, direct interaction between the technologies occurred only in 3 (19%) of the 16 studies. The included studies all show very limited quality evidence. From the patients' and healthy individuals' perspective, generally high usability, motivation, and satisfaction were found.

CONCLUSIONS

The review shows limited quality of evidence for the combination of AI and games in health care. Most of the included studies were nonrandomized pilot studies with few participants (14/16, 88%). This leads to a high risk for a range of biases and limits overall conclusions. However, the first results present a broad scope of possible applications, especially in motion and cognitive impairment, as well as positive perceptions by patients. In future, the development of adaptive game designs with direct interaction between AI and games seems promising and should be a topic for future reviews.

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.

Triboelectric Contact Localization Electronics: A Systematic Review

The growing demand from the extended reality and wearable electronics market has led to an increased focus on the development of flexible human-machine interfaces (HMI). These interfaces require efficient user input acquisition modules that can realize touch operation, handwriting input, and motion sensing functions. In this paper, we present a systematic review of triboelectric-based contact localization electronics (TCLE) which play a crucial role in enabling the lightweight and long-endurance designs of flexible HMI. We begin by summarizing the mainstream working principles utilized in the design of TCLE, highlighting their respective strengths and weaknesses. Additionally, we discuss the implementation methods of TCLE in realizing advanced functions such as sliding motion detection, handwriting trajectory detection, and artificial intelligence-based user recognition. Furthermore, we review recent works on the applications of TCLE in HMI devices, which provide valuable insights for guiding the design of application scene-specified TCLE devices. Overall, this review aims to contribute to the advancement and understanding of TCLE, facilitating the development of next-generation HMI for various applications.

Self-powered freestanding multifunctional microneedle-based extended gate device for personalized health monitoring

Online monitoring of prognostic biomarkers is critically important when diagnosing disorders and assessing individuals' health, especially for chronic and infectious diseases. Despite this, current diagnosis techniques are time-consuming, labor-intensive, and performed offline. In this context, developing wearable devices for continuous measurements of multiple biomarkers from body fluids has considerable advantages including availability, rapidity, convenience, and minimal invasiveness over the conventional painful and time-consuming tools. However, there is still a significant challenge in powering these devices over an extended period, especially for applications that require continuous and long-term health monitoring. Herein, a new freestanding, wearable, multifunctional microneedle-based extended gate field effect transistor biosensor is fabricated for online detection of multiple biomarkers from the interstitial fluid including sodium, calcium, potassium, and pH along with excellent electrical response, reversibility, and precision. In addition, a hybrid powering system of triboelectric nanogenerator and solar cell was developed for creating a freestanding, closed-loop platform for continuous charging of the device's battery and integrated with an Internet of Things technology to broadcast the measurements online, suggesting a stand-alone, stable multifunctional tool which paves the way for advanced practical personalized health monitoring and diagnosis.

Doping Engineering of Conductive Polymers and Their Application in Physical Sensors for Healthcare Monitoring

Physical sensors have emerged as a promising technology for real-time healthcare monitoring, which tracks various physical signals from the human body. Accurate acquisition of these physical signals from biological tissue requires excellent electrical conductivity and long-term durability of the sensors under complex mechanical deformation. Conductive polymers, combining the advantages of conventional polymers and organic conductors, are considered ideal conductive materials for healthcare physical sensors due to their intrinsic conductive network, tunable mechanical properties, and easy processing. Doping engineering has been proposed as an effective approach to enhance the sensitivity, lower the detection limit, and widen the operational range of sensors based on conductive polymers. This approach enables the introduction of dopants into conductive polymers to adjust and control the microstructure and energy levels of conductive polymers, thereby optimizing their mechanical and conductivity properties. This review article provides a comprehensive overview of doping engineering methods to improve the physical properties of conductive polymers and highlights their applications in the field of healthcare physical sensors, including temperature sensors, strain sensors, stress sensors, and electrophysiological sensing. Additionally, the challenges and opportunities associated with conductive polymer-based physical sensors in healthcare monitoring are discussed.

3D Printed Conformal Strain and Humidity Sensors for Human Motion Prediction and Health Monitoring via Machine Learning

Wearable sensors have garnered considerable attention due to their flexibility and lightweight characteristics in the realm of healthcare applications. However, developing robust wearable sensors with facile fabrication and good conformity remains a challenge. In this study, a conductive graphene nanoplate-carbon nanotube (GC) ink is synthesized for multi jet fusion (MJF) printing. The layer-by-layer fabrication process of MJF not only improves the mechanical and flame-retardant properties of the printed GC sensor but also bolsters its robustness and sensitivity. The direction of sensor bending significantly impacts the relative resistance changes, allowing for precise investigations of joint motions in the human body, such as those of the fingers, wrists, elbows, necks, and knees. Furthermore, the data of resistance changes collected by the GC sensor are utilized to train a support vector machine with a 95.83% accuracy rate for predicting human motions. Due to its stable humidity sensitivity, the sensor also demonstrates excellent performance in monitoring human breath and predicting breath modes (normal, fast, and deep breath), thereby expanding its potential applications in healthcare. This work opens up new avenues for using MJF-printed wearable sensors for a variety of healthcare applications.

Flexible wearable intelligent sensing system for wheelchair sports monitoring

The application of wearable intelligent systems toward human-computer interaction has received widespread attention. It is still desirable to conveniently promote health and monitor sports skills for disabled people. Here, a wireless intelligent sensing system (WISS) has been developed, which includes two ports of wearable flexible triboelectric nanogenerator (WF-TENG) sensing and an upper computer digital signal receiving intelligent processing. The WF-TENG sensing port is connected by the WF-TENG sensor and flexible printed circuit (FPC). Due to its flexibility, the WF-TENG sensing port can be freely adhered on the surface of human skin. The WISS can be applied to entertainment reaction training based on human-computer interaction, and to the technical judgment and analysis on wheelchair curling sport. This work provides new application opportunities for wearable devices in the fields of sports skills monitoring, sports assistive devices and health promotion for disabled people.

Metaverse Wearables for Immersive Digital Healthcare: A Review

The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.

A Fully Self-Powered Wearable Leg Movement Sensing System for Human Health Monitoring

Energy-autonomous wearable human activity monitoring is imperative for daily healthcare, benefiting from long-term sustainable uses. Herein, a fully self-powered wearable system, enabling real-time monitoring and assessments of human multimodal health parameters including knee joint movement, metabolic energy, locomotion speed, and skin temperature, which are fully self-powered by highly-efficient flexible thermoelectric generators (f-TEGs) is proposed and developed. The wearable system is composed of f-TEGs, fabric strain sensors, ultra-low-power edge computing, and Bluetooth. The f-TEGs worn on the leg not only harvest energy from body heat and supply power sustainably for the whole monitoring system, but also serve as zero-power motion sensors to detect limb movement and skin temperature. The fabric strain sensor made by printing PEDOT: PSS on pre-stretched nylon fiber-wrapped rubber band enables high-fidelity and ultralow-power measurements on highly-dynamic knee movements. Edge computing is elaborately designed to estimate multimodal health parameters including time-varying metabolic energy in real-time, which are wirelessly transmitted via Bluetooth. The whole monitoring system is operated automatically and intelligently, works sustainably in both static and dynamic states, and is fully self-powered by the f-TEGs.

Reusable and Porous Skin Patches with Thermopneumatic Adhesion Control Capability and High Water Vapor Permeability

We present a reusable and porous skin patch (RPS patch) capable of controlling adhesion force with a thermal-pneumatic method for repetitive use as well as improving moisture permeability for long-term use without skin troubles. Previous skin patches cause skin troubles due to high adhesion force (∼30 kPa) and low moisture permeability (∼382 g/m2/day), hindering them from repeatable and long-term use. We control the skin adhesion force of the RPS patch using thermopneumatic pressure generated by an embedded heater on multiple chamber arrays. The RPS patch controls the adhesion force ranging from 8 to 29 kPa on both dry and wet skin while keeping the stable adhesion force for 48 h. It shows repeatable adhesion up to 100 times, and the adhesion force is restored after the RPS patch is washed with water, thus enabling repetitive skin adhesion. We improve the moisture permeability of the RPS patch to 733 g/m2/day while maintaining the adhesion force by making the RPS patch with porous materials. The RPS patch shows no skin troubles for 7 days of attachment, thereby being available for long-term skin attachment. The RPS patch, having adhesion control capability and high moisture permeability, shows potential for use in daily life in biomedical applications, including wearable sensors, medical adhesives, and rehabilitation robots.

Structure-Crack Detection and Digital Twin Demonstration Based on Triboelectric Nanogenerator for Intelligent Maintenance

The accomplishment of condition monitoring and intelligent maintenance for cantilever structure-based energy harvesting devices remains a challenge. Here, to tackle the problems, a novel cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) is proposed, which can capture ambient energy or transmit sensory information. First, with and without a crack in cantilevers, the simulations are carried out. According to simulation results, the maximum change ratios of natural frequency and amplitude are 1.1% and 2.2%, causing difficulties in identifying defects by these variations. Thus, based on Gramian angular field and convolutional neural network, a defect detection model is established to achieve the condition monitoring of the CSF-TENG, and the experimental result manifests that the accuracy of the model is 99.2%. Besides, the relation between the deflection of cantilevers and the output voltages of the CSF-TENG is first built, and then the defect identification digital twin system is successfully created. Consequently, the system is capable of duplicating the operation of the CSF-TENG in a real environment, and displaying defect recognition results, so the intelligent maintenance of the CSF-TENG can be realized.

Fabrication of a textile-based triboelectric nanogenerator toward high-efficiency energy harvesting and material recognition

Textile-based triboelectric nanogenerator (T-TENG) devices, particularly, narrow-gap mode, have been conceived and developed for obtaining energy harvesting and tactile sensing devices unaffected by the external environment. Enhancing the interfacial area of T-TENG materials offers exciting opportunities to improve the device output performance. In this work, a narrow-gap T-TENG was fabricated with a facile process, and a new strategy for improving the device output is proposed. The new structural sensor (polydimethylsiloxane (PDMS)-encapsulated electroless copper plating (EP-Cu) cotton) with multiple electricity generation mechanism was designed and fabricated for enhancing recognition accuracy. The result shows that only PDMS layer strain was established at an external stress of 1.24-12.4 kPa and the fibers laterally slip at a stress of 12.4-139 kPa; more importantly, the output performance of the TENG displayed a linear relationship under corresponding stress ranges. The as-fabricated device demonstrated the ability to convert different energies such as vibration, raindrops, wind and human motions into electrical energy with excellent sensitivity. Interestingly, the output signal of the as-fabricated TENG device is a combination of output signals from PDMS/EP-Cu and PDMS/recognition object devices. To be precise, there are two TENG devices (PDMS/EP-Cu and PDMS/recognition object) that work when the as-fabricated TENG device is under 12.4-139 kPa stress. Accompanied by unique characteristics, the generated TENG signals are capable of recognition of contact materials. Combining the TENG signal and deep learning technology, we explore a strategy that can enable the as-fabricated device to recognize 8 different materials with 99.48% recognition accuracy in the natural environment.

Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

A Light-Weight Artificial Neural Network for Recognition of Activities of Daily Living

Human activity recognition (HAR) is essential for the development of robots to assist humans in daily activities. HAR is required to be accurate, fast and suitable for low-cost wearable devices to ensure portable and safe assistance. Current computational methods can achieve accurate recognition results but tend to be computationally expensive, making them unsuitable for the development of wearable robots in terms of speed and processing power. This paper proposes a light-weight architecture for recognition of activities using five inertial measurement units and four goniometers attached to the lower limb. First, a systematic extraction of time-domain features from wearable sensor data is performed. Second, a small high-speed artificial neural network and line search method for cost function optimization are used for activity recognition. The proposed method is systematically validated using a large dataset composed of wearable sensor data from seven activities (sitting, standing, walking, stair ascent/descent, ramp ascent/descent) associated with eight healthy subjects. The accuracy and speed results are compared against methods commonly used for activity recognition including deep neural networks, convolutional neural networks, long short-term memory and convolutional-long short-term memory hybrid networks. The experiments demonstrate that the light-weight architecture can achieve a high recognition accuracy of 98.60%, 93.10% and 84.77% for seen data from seen subjects, unseen data from seen subjects and unseen data from unseen subjects, respectively, and an inference time of 85 μs. The results show that the proposed approach can perform accurate and fast activity recognition with a reduced computational complexity suitable for the development of portable assistive devices.

A Wearable Multidimensional Motion Sensor for AI-Enhanced VR Sports

Regular exercise paves the way to a healthy life. However, conventional sports events are susceptible to weather conditions. Current motion sensors for home-based sports are mainly limited by operation power consumption, single-direction sensitivity, or inferior data analysis. Herein, by leveraging the 3-dimensional printing technique and triboelectric effect, a wearable self-powered multidimensional motion sensor has been developed to detect both the vertical and planar movement trajectory. By integrating with a belt, this sensor could be used to identify some low degree of freedom motions, e.g., waist or gait motion, with a high accuracy of 93.8%. Furthermore, when wearing the sensor at the ankle position, signals generated from shank motions that contain more abundant information could also be effectively collected. By means of a deep learning algorithm, the kicking direction and force could be precisely differentiated with an accuracy of 97.5%. Toward practical application, a virtual reality-enabled fitness game and a shooting game were successfully demonstrated. This work is believed to open up new insights for the development of future household sports or rehabilitation.

High-Precision Wearable Displacement Sensing System for Clinical Diagnosis of Anterior Cruciate Ligament Tears

An anterior cruciate ligament (ACL) tear is a common musculoskeletal injury with a high incidence. Traditional diagnosis employs magnetic response imaging (MRI), physical testing, or other clinical examination, which relies on complex and expensive medical instruments, or individual doctoral experience. Herein, we propose a wearable displacement sensing system based on a grating-structured triboelectric stretch sensor to diagnose the ACL injuries. The stretch sensor exhibits a high resolution (0.2 mm) and outstanding robustness (over 1,000,000 continuous operation cycles). This system is employed in clinical trial to diagnose ACL injuries. It measures the displacement difference between the affected leg and the healthy leg during Lachman test. And when such a difference is greater than 3 mm, the ACL is considered to be at risk for injury or tear. Compared with the gold standard of arthroscopy, the consistency rate of this wearable diagnostic system reached about 85.7%, which is higher than that of the Kneelax3 arthrometer (78.6%) with a large volume. This shows that the wearable system possesses the feasibility to supplement and improve existing arthrometers for facile diagnosing ACL injuries. It may take a promising step for wearable healthcare.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

Recent advancements in digital health management using multi-modal signal monitoring

Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term "Internet of Medical Things" (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.

Intelligent wearable devices based on nanomaterials and nanostructures for healthcare

Emerging classes of flexible electronic sensors as alternatives to conventional rigid sensors offer a powerful set of capabilities for detecting and quantifying physiological and physical signals from human skin in personal healthcare. Unfortunately, the practical applications and commercialization of flexible sensors are generally limited by certain unsatisfactory aspects of their performance, such as biocompatibility, low sensing range, power supply, or single sensory function. This review intends to provide up-to-date literature on wearable devices for smart healthcare. A systematic review is provided, from sensors based on nanomaterials and nanostructures, algorithms, to multifunctional integrated devices with stretchability, self-powered performance, and biocompatibility. Typical electromechanical sensors are investigated with a specific focus on the strategies for constructing high-performance sensors based on nanomaterials and nanostructures. Then, the review emphasizes the importance of tailoring the fabrication techniques in order to improve stretchability, biocompatibility, and self-powered performance. The construction of wearable devices with high integration, high performance, and multi-functionalization for multiparameter healthcare is discussed in depth. Integrating wearable devices with appropriate machine learning algorithms is summarized. After interpretation of the algorithms, intelligent predictions are produced to give instructions or predictions for smart implementations. It is desired that this review will offer guidance for future excellence in flexible wearable sensing technologies and provide insight into commercial wearable sensors.

Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle

The analysis of gait patterns and plantar pressure distributions via insoles is increasingly used to monitor patients and treatment progress, such as recovery after surgeries. Despite the popularity of pedography, also known as baropodography, characteristic effects of anthropometric and other individual parameters on the trajectory of the stance phase curve of the gait cycle have not been previously reported. We hypothesized characteristic changes of age, body height, body weight, body mass index and handgrip strength on the plantar pressure curve trajectory during gait in healthy participants. Thirty-seven healthy women and men with an average age of 43.65 ± 17.59 years were fitted with Moticon OpenGO insoles equipped with 16 pressure sensors each. Data were recorded at a frequency of 100 Hz during walking at 4 km/h on a level treadmill for 1 minute. Data were processed via a custom-made step detection algorithm. The loading and unloading slopes as well as force extrema-based parameters were computed and characteristic correlations with the targeted parameters were identified via multiple linear regression analysis. Age showed a negative correlation with the mean loading slope. Body height correlated with Fmean_load and the loading slope. Body weight and the body mass index correlated with all analyzed parameters, except the loading slope. In addition, handgrip strength correlated with changes in the second half of the stance phase and did not affect the first half, which is likely due to stronger kick-off. However, only up to 46% of the variability can be explained by age, body weight, height, body mass index and hand grip strength. Thus, further factors must affect the trajectory of the gait cycle curve that were not considered in the present analysis. In conclusion, all analyzed measures affect the trajectory of the stance phase curve. When analyzing insole data, it might be useful to correct for the factors that were identified by using the regression coefficients presented in this paper.

Touch IoT enabled by wireless self-sensing and haptic-reproducing electronic skin

Tactile sensations are mainly transmitted to each other by physical touch. Wireless touch perception could be a revolution for us to interact with the world. Here, we report a wireless self-sensing and haptic-reproducing electronic skin (e-skin) to realize noncontact touch communications. A flexible self-sensing actuator was developed to provide an integrated function in both tactile sensing and haptic feedback. When this e-skin was dynamically pressed, the actuator generated an induced voltage as tactile information. Via wireless communication, another e-skin could receive this tactile data and run a synchronized haptic reproduction. Thus, touch could be wirelessly conveyed in bidirections between two users as a touch intercom. Furthermore, this e-skin could be connected with various smart devices to form a touch internet of things where one-to-one and one-to-multiple touch delivery could be realized. This wireless touch presents huge potentials in remote touch video, medical care/assistance, education, and many other applications.

Multidiscipline Applications of Triboelectric Nanogenerators for the Intelligent Era of Internet of Things

In the era of 5G and the Internet of things (IoTs), various human-computer interaction systems based on the integration of triboelectric nanogenerators (TENGs) and IoTs technologies demonstrate the feasibility of sustainable and self-powered functional systems. The rapid development of intelligent applications of IoTs based on TENGs mainly relies on supplying the harvested mechanical energy from surroundings and implementing active sensing, which have greatly changed the way of human production and daily life. This review mainly introduced the TENG applications in multidiscipline scenarios of IoTs, including smart agriculture, smart industry, smart city, emergency monitoring, and machine learning-assisted artificial intelligence applications. The challenges and future research directions of TENG toward IoTs have also been proposed. The extensive developments and applications of TENG will push forward the IoTs into an energy autonomy fashion.

0.5 m Triboelectric Nanogenerator for Efficient Blue Energy Harvesting of All-Sea Areas

Triboelectric nanogenerators (TENGs) to harvest ocean wave blue energy is flourishing, yet the research horizon has been limited to centimeter-level TENG. Here, for the first time, a TENG shell is advanced for ocean energy harvesting to 0.5 m and an excellent frictional areal density of 1.03 cm^-1 and economies of scale are obtained. The unique structure of the multi-arch shape is adopted to untie the difficulty of fully getting the extensive friction layer contact. An inside steel plate is vertically placed in the center of every TENG block, which can activate the TENG to achieve complete contact even at a tilt angle of 7 degrees. The proposed half-meter TENG (HM-TENG) has a broad response band from 0.1 to 2 Hz, a total transferred charge quantity up to 67.2 µC, and one single TENG can deliver an open-circuit voltage of 368 V. Coupled with the self-stabilizing and susceptible features the ellipsoid shell brings, the HM-TENG can readily accommodate itself to the all-weather, all-sea wave energy harvesting. Muchmore, the HM-TENG is also applied to RF signal transmitters. This work takes the first step toward near-meter-scale enclosures and provides a new direction for large-scale wave energy harvesting.

Application of High-Photoelasticity Polyurethane to Tactile Sensor for Robot Hands

We developed a tactile sensor for robot hands that can measure normal force (F_Z) and tangential forces (F_X and F_Y) using photoelasticity. This tactile sensor has three photodiodes and three light-emitting diode (LED) white light sources. The sensor is composed of multiple elastic materials, including a highly photoelastic polyurethane sheet, and the sensor can detect both normal and tangential forces through the deformation, ben sding, twisting, and extension of the elastic materials. The force detection utilizes the light scattering resulting from birefringence.

Soft, Multifunctional, Robust Film Sensor Using a Ferroelectret with Significant Longitudinal and Transverse Piezoelectric Activity for Biomechanical Monitoring

Soft and intelligent bioelectronics have achieved unprecedented development in both academics and industries over the last few decades, especially as ideal body-worn detectors for continuous human health status monitoring. However, the longstanding functional stability of bioelectronics in multiple environmental conditions of variant temperatures, humidities, and mechanical stimuli or even in some extremes, such as ultraviolet radiation and X-ray radiation, has confined the application of these electronics. Herein, a self-sustainable, multifunctional, robust sensor for biomechanical monitoring is prepared by hybridizing a parallel-tunnel fluorinated poly(ethylene propylene) (FEP) ferroelectret film (sensing layer) and poly(dimethylsiloxane) (PDMS, protection layer). A fast response (80 ms) and a low pressure detection limit (10 Pa) were achieved. Notably, the self-powered sensor can not only sensitively detect the loading of solid objects but also percept liquid water droplets and airflow, which satisfies the diverse needs of wearable devices. Meanwhile, the capability of stable and repeatable operation under a wide temperature range (-26-70 °C), extreme moisture, continuous mechanical stimulus (∼1.08 million cycles), and long-time ultraviolet radiation enabled the extensive and long-term application of such sensors in multiple scenarios. Moreover, the reproducibility of sensing performance after X-ray radiation can be realized through second contact polarization even after encapsulation. Due to the inherent mechanical flexibility, the fabricated sensor was conformally attached to rough and deformed skin and verified the feasibility of wearable biomechanical sensing with high sensitivity from facial smiling to plantar movement. This work provides an efficient strategy for multifunctional sensing, holding great promise for advanced soft bioelectronics in the next generation of wearable intelligent electronic systems.

Human Machine Interface with Wearable Electronics Using Biodegradable Triboelectric Films for Calligraphy Practice and Correction

Letter handwriting, especially stroke correction, is of great importance for recording languages and expressing and exchanging ideas for individual behavior and the public. In this study, a biodegradable and conductive carboxymethyl chitosan-silk fibroin (CSF) film is prepared to design wearable triboelectric nanogenerator (denoted as CSF-TENG), which outputs of Voc ≈ 165 V, Isc ≈ 1.4 μA, and Qsc ≈ 72 mW cm-2. Further, in vitro biodegradation of CSF film is performed through trypsin and lysozyme. The results show that trypsin and lysozyme have stable and favorable biodegradation properties, removing 63.1% of CSF film after degrading for 11 days. Further, the CSF-TENG-based human-machine interface (HMI) is designed to promptly track writing steps and access the accuracy of letters, resulting in a straightforward communication media of human and machine. The CSF-TENG-based HMI can automatically recognize and correct three representative letters (F, H, and K), which is benefited by HMI system for data processing and analysis. The CSF-TENG-based HMI can make decisions for the next stroke, highlighting the stroke in advance by replacing it with red, which can be a candidate for calligraphy practice and correction. Finally, various demonstrations are done in real-time to achieve virtual and real-world controls including writing, vehicle movements, and healthcare.

A Comprehensive Review of the Recent Developments in Wearable Sweat-Sensing Devices

Sweat analysis offers non-invasive real-time on-body measurement for wearable sensors. However, there are still gaps in current developed sweat-sensing devices (SSDs) regarding the concerns of mixing fresh and old sweat and real-time measurement, which are the requirements to ensure accurate the measurement of wearable devices. This review paper discusses these limitations by aiding model designs, features, performance, and the device operation for exploring the SSDs used in different sweat collection tools, focusing on continuous and non-continuous flow sweat analysis. In addition, the paper also comprehensively presents various sweat biomarkers that have been explored by earlier works in order to broaden the use of non-invasive sweat samples in healthcare and related applications. This work also discusses the target analyte's response mechanism for different sweat compositions, categories of sweat collection devices, and recent advances in SSDs regarding optimal design, functionality, and performance.

Interpretable evaluation for the Brunnstrom recovery stage of the lower limb based on wearable sensors

With the increasing number of stroke patients, there is an urgent need for an accessible, scientific, and reliable evaluation method for stroke rehabilitation. Although many rehabilitation stage evaluation methods based on the wearable sensors and machine learning algorithm have been developed, the interpretable evaluation of the Brunnstrom recovery stage of the lower limb (BRS-L) is still lacking. The paper propose an interpretable BRS-L evaluation method based on wearable sensors. We collected lower limb motion data and plantar pressure data of 20 hemiplegic patients and 10 healthy individuals using seven Inertial Measurement Units (IMUs) and two plantar pressure insoles. Then we extracted gait features from the motion data and pressure data. By using feature selection based on feature importance, we improved the interpretability of the machine learning-based evaluation method. Several machine learning models are evaluated on the dataset, the results show that k-Nearest Neighbor has the best prediction performance and achieves 94.2% accuracy with an input of 18 features. Our method provides a feasible solution for precise rehabilitation and home-based rehabilitation of hemiplegic patients.

Hybrid nanogenerator based closed-loop self-powered low-level vagus nerve stimulation system for atrial fibrillation treatment

Atrial fibrillation is an "invisible killer" of human health. It often induces high-risk diseases, such as myocardial infarction, stroke, and heart failure. Fortunately, atrial fibrillation can be diagnosed and treated early. Low-level vagus nerve stimulation (LL-VNS) is a promising therapeutic method for atrial fibrillation. However, some fundamental challenges still need to be overcome in terms of flexibility, miniaturization, and long-term service of bioelectric stimulation devices. Here, we designed a closed-loop self-powered LL-VNS system that can monitor the patient's pulse wave status in real time and conduct stimulation impulses automatically during the development of atrial fibrillation. The implant is a hybrid nanogenerator (H-NG), which is flexible, light weight, and simple, even without electronic circuits, components, and batteries. The maximum output of the H-NG was 14.8 V and 17.8 μA (peak to peak). In the in vivo effect verification study, the atrial fibrillation duration significantly decreased by 90% after LL-VNS therapy, and myocardial fibrosis and atrial connexin levels were effectively improved. Notably, the anti-inflammatory effect triggered by mediating the NF-κB and AP-1 pathways in our therapeutic system is observed. Overall, this implantable bioelectronic device is expected to be used for self-powerability, intelligentization, portability for management, and therapy of chronic diseases.