Artificial Intelligence of Things (AIoT) Enabled Floor Monitoring System for Smart Home Applications

Triboelectric Nanogenerator-Enabled Digital Twins in Civil Engineering Infrastructure 4.0: A Comprehensive Review

The emergence of digital twins has ushered in a new era in civil engineering with a focus on achieving sustainable energy supply, real-time sensing, and rapid warning systems. These key development goals mean the arrival of Civil Engineering 4.0.The advent of triboelectric nanogenerators (TENGs) demonstrates the feasibility of energy harvesting and self-powered sensing. This review aims to provide a comprehensive analysis of the fundamental elements comprising civil infrastructure, encompassing various structures such as buildings, pavements, rail tracks, bridges, tunnels, and ports. First, an elaboration is provided on smart engineering structures with digital twins. Following that, the paper examines the impact of using TENG-enabled strategies on smart civil infrastructure through the integration of materials and structures. The various infrastructures provided by TENGs have been analyzed to identify the key research interest. These areas encompass a wide range of civil infrastructure characteristics, including safety, efficiency, energy conservation, and other related themes. The challenges and future perspectives of TENG-enabled smart civil infrastructure are briefly discussed in the final section. In conclusion, it is conceivable that in the near future, there will be a proliferation of smart civil infrastructure accompanied by sustainable and comprehensive smart services.

Haptic Sensing and Feedback Techniques toward Virtual Reality

Haptic interactions between human and machines are essential for information acquisition and object manipulation. In virtual reality (VR) system, the haptic sensing device can gather information to construct virtual elements, while the haptic feedback part can transfer feedbacks to human with virtual tactile sensation. Therefore, exploring high-performance haptic sensing and feedback interface imparts closed-loop haptic interaction to VR system. This review summarizes state-of-the-art VR-related haptic sensing and feedback techniques based on the hardware parts. For the haptic sensor, we focus on mechanism scope (piezoresistive, capacitive, piezoelectric, and triboelectric) and introduce force sensor, gesture translation, and touch identification in the functional view. In terms of the haptic feedbacks, methodologies including mechanical, electrical, and elastic actuators are surveyed. In addition, the interactive application of virtual control, immersive entertainment, and medical rehabilitation is also summarized. The challenges of virtual haptic interactions are given including the accuracy, durability, and technical conflicts of the sensing devices, bottlenecks of various feedbacks, as well as the closed-loop interaction system. Besides, the prospects are outlined in artificial intelligence of things, wise information technology of medicine, and multimedia VR areas.

Highly Sensitive and Stable Multifunctional Self-Powered Triboelectric Sensor Utilizing Mo2CTx/PDMS Composite Film for Pressure Sensing and Non-Contact Sensing

Sensors based on triboelectric nanogenerators (TENGs) are increasingly gaining attention because of their self-powered capabilities and excellent sensing performance. In this work, we report a Mo2CTx-based triboelectric sensor (Mo-TES) consisting of a Mo2CTx/polydimethylsiloxane (PDMS) composite film. The impact of the mass fraction (wt%) and force of Mo2CTx particles on the output performance of Mo-TES was systematically explored. When Mo2CTx particles is 3 wt%, Mo-TES3 achieves an open-circuit voltage of 86.89 V, a short-circuit current of 578.12 nA, and a power density of 12.45 μW/cm2. It also demonstrates the ability to charge capacitors with varying capacitance values. Additionally, the Mo-TES3 demonstrates greater sensitivity than the Mo-TES0 and a faster recovery time of 78 ms. Meanwhile, the Mo-TES3 also demonstrates excellent stability in water washing and antifatigue testing. This demonstrates the effectiveness of Mo-TES as a pressure sensor. Furthermore, leveraging the principle of electrostatic induction, the triboelectric sensor has the potential to achieve non-contact sensing, making it a promising candidate for disease prevention and safety protection.

A shadow enabled non-invasive probe for multi-feature intelligent liquid surveillance system

Liquid detection probes used to identify the features of liquids show great promise in a variety of important applications. However, some challenges, such as sample contamination by direct contact with the liquid, the requirement of additional signal emitters, and complex fabrication, hindered the development of liquid detection probes. Here, we developed a non-invasive shadow probe (SP) for a multi-feature intelligent liquid surveillance system (ILSS). The self-powered SP with the working mechanism of the shadow effect can detect the features of liquids by analyzing the variations of liquid shadows such as the area, wavelength, and brightness. The exact resolution (5 different colors, 6 different concentrations, 6 different levels, 100% accuracy) and fast response time (0.2 ms) are shown by the SP under ambient light conditions (even in 0.003 sun). The ILSS, which integrated the SPs with signal processing circuits and applied the artificial intelligence (AI) technique, successfully detects and synoptically learns about liquids simultaneously. The in-real time ILSS reaches a test accuracy of 99.3% for 10 types of liquids with multiple features. This work showcases a promising solution for non-invasive multi-feature liquid detection, displaying great potential for future applications.

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.

A Natural Gradient Biological-Enabled Multimodal Triboelectric Nanogenerator for Driving Safety Monitoring

A key element to ensuring driving safety is to provide a sufficient braking distance. Inspired by the nature triply periodic minimal surface (TPMS), a gradient and multimodal triboelectric nanogenerator (GM-TENG) is proposed with high sensitivity and excellent multimodal monitoring. The gradient TPMS structure exhibits the multi-stage stress-strain properties of typical porous metamaterials. Significantly, the multimodal monitoring capability depends on the implicit function of the defined level constant c, which directly contributes to the multimodal driving safety monitoring. The mechanical and electrical responsive behavior of the GM-TENG is analyzed to identify the applied speed, load, and working mode. In addition, optimized peak open-circuit voltage (Voc) is demonstrated for self-awareness of the braking condition. The braking distance factor (L) is conceived to construct the self-aware equation of the friction coefficient based on the integration of Voc with respect to time. Importantly, R-squared up to 94.29 % can be obtained, which improves self-aware accuracy and real-time capabilities. This natural structure and self-aware device provide an effective strategy to improve driving safety, which contributes to the improvement of road safety and presents self-powered sensing with potential applications in an intelligent transportation system.

Intelligent soft robotic fingers with multi-modality perception ability

In the context of industry 4.0, automatic sorting is becoming prevalent in production lines. Herein, we developed a bionic sensing system to achieve real-time object recognition. The system consists of 9 single-layer triboelectric nanogenerators (SL-TENGs) as touch sensors and 3 comb-shaped TENGs (CS-TENGs) as bending sensors, with a sensitivity of 110 V/kPa and stable output after 20,000 press cycles. These sensors were attached to a manipulator composed of three soft actuators, serving as soft robotic fingers. An enhanced electrical output of these sensors was achieved successfully, demonstrating their feasibility in detecting grasping location, contact pressure, and bending curvature. A one-dimensional convolutional neural network (1D-CNN) with 98.96% accuracy extracted information from the sensors, enabling the manipulator to serve as an intelligent sensing system with multi-modality perception ability. This robotic manipulator successfully integrated TENG-based self-powered sensors, soft actuators, and artificial intelligence, demonstrating the potential for future digital twin applications, particularly in automatic component sorting.

Research Progress on the Application of Triboelectric Nanogenerators for Wind Energy Collection

The escalating global energy demand necessitates the exploration of renewable energy sources, with wind energy emerging as a crucial and widely available resource. With wind energy exhibiting a vast potential of approximately 1010 kw/a per year, about ten times that of global hydroelectric power generation, its efficient conversion and utilization hold the promise of mitigating the pressing energy crisis and replacing the dominant reliance on fossil fuels. In recent years, Triboelectric Nanogenerators (TENGs) have emerged as novel and efficient means of capturing wind energy. This paper provides a comprehensive summary of the fundamental principles governing four basic working modes of TENGs, elucidating the structures and operational mechanisms of various models employed in wind energy harvesting. Furthermore, it highlights the significance of two major TENG configurations, namely, the vertical touch-separation pattern structure and the independent layer pattern for wind energy collection, emphasizing their respective advantages. Furthermore, the study briefly discusses the current strengths of nano-friction power generation in wind energy harvesting while acknowledging the existing challenges pertaining to device design, durability, operation, and maintenance. The review concludes by presenting potential research directions and prospects for triboelectric nanogenerators generation in the realm of wind energy, offering valuable insights for researchers and scholars in the field.

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.

Dual-sensory fusion self-powered triboelectric taste-sensing system towards effective and low-cost liquid identification

Infusing human taste perception into smart sensing devices to mimic the processing ability of gustatory organs to perceive liquid substances remains challenging. Here we developed a self-powered droplet-tasting sensor system based on the dynamic morphological changes of droplets and liquid-solid contact electrification. The sensor system has achieved accuracies of liquid recognition higher than 90% in five different applications by combining triboelectric fingerprint signals and deep learning. Furthermore, an image sensor is integrated to extract the visual features of liquids, and the recognition capability of the liquid-sensing system is improved to up to 96.0%. The design of this dual-sensory fusion self-powered liquid-sensing system, along with the droplet-tasting sensor that can autonomously generate triboelectric signals, provides a promising technological approach for the development of effective and low-cost liquid sensing for liquid food safety identification and management.

Design of 3D Controller Using Nanocracking Structure-Based Stretchable Strain Sensor

In this study, we introduce a novel design for a three-dimensional (3D) controller, which incorporates the omni-purpose stretchable strain sensor (OPSS sensor). This sensor exhibits both remarkable sensitivity, with a gauge factor of approximately 30, and an extensive working range, accommodating strain up to 150%, thereby enabling accurate 3D motion sensing. The 3D controller is structured such that its triaxial motion can be discerned independently along the X, Y, and Z axes by quantifying the deformation of the controller through multiple OPSS sensors affixed to its surface. To ensure precise and real-time 3D motion sensing, a machine learning-based data analysis technique was implemented for the effective interpretation of the multiple sensor signals. The outcomes reveal that the resistance-based sensors successfully and accurately track the 3D controller's motion. We believe that this innovative design holds the potential to augment the performance of 3D motion sensing devices across a diverse range of applications, encompassing gaming, virtual reality, and robotics.

Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)

The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10-46 Hz, with a record high power density of 17 mW/cm³ at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%-100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation.

Mechanochromic Optical/Electrical Skin for Ultrasensitive Dual-Signal Sensing

Following earlier research efforts dedicated to the realization of multifunctional sensing, recent developments of artificial skins endeavor to go beyond human sensory functions by integrating interactive visualization of strain and pressure stimuli. Inspired by the microcracked structure of spider slit organs and the mechanochromic mechanism of chameleons, this work aims to design a flexible optical/electrical skin (OE-skin) capable of responding to complex stimuli with interactive feedback of human-readable structural colors. The OE-skin consists of an ionic electrode combined with an elastomer dielectric layer, a chromotropic layer containing photonic crystals and a conductive carbon nanotube/MXene layer. The electrode/dielectric layers function as a capacitive pressure sensor. The mechanochromic photonic crystals of ferroferric oxide-carbon magnetic arrays embedded in the gelatin/polyacrylamide stretchable hydrogel film perceive strain and pressure stimuli with bright color switching outputs in the full visible spectrum. The underlying microcracked conductive layer is devoted to ultrasensitive strain sensing with a gauge factor of 191.8. The multilayered OE-skin delivers an ultrafast, accurate response for capacitive pressure sensing with a detection limit of 75 Pa and long-term stability of 5000 cycles, while visualizing complex deformations in the form of high-resolution spatial colors. These findings offer deep insights into the rational design of OE-skins as multifunctional sensing devices.

Static Hand Gesture Recognition Using Capacitive Sensing and Machine Learning

Automated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a 6×18 array of capacitive sensors that captured five gestures-Palm, Fist, Middle, OK, and Index-of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants' gestures and tested with one different participant's gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average F1 score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system.

Soft Robotic Perception System with Ultrasonic Auto-Positioning and Multimodal Sensory Intelligence

Flexible electronics such as tactile cognitive sensors have been broadly adopted in soft robotic manipulators to enable human-skin-mimetic perception. To achieve appropriate positioning for randomly distributed objects, an integrated guiding system is inevitable. Yet the conventional guiding system based on cameras or optical sensors exhibits limited environment adaptability, high data complexity, and low cost effectiveness. Herein, a soft robotic perception system with remote object positioning and a multimodal cognition capability is developed by integrating an ultrasonic sensor with flexible triboelectric sensors. The ultrasonic sensor is able to detect the object shape and distance by reflected ultrasound. Thereby the robotic manipulator can be positioned to an appropriate position to perform object grasping, during which the ultrasonic and triboelectric sensors can capture multimodal sensory information such as object top profile, size, shape, hardness, material, etc. These multimodal data are then fused for deep-learning analytics, leading to a highly enhanced accuracy in object identification (∼100%). The proposed perception system presents a facile, low-cost, and effective methodology to integrate positioning capability with multimodal cognitive intelligence in soft robotics, significantly expanding the functionalities and adaptabilities of current soft robotic systems in industrial, commercial, and consumer applications.

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.

Improving Relative Permittivity and Suppressing Dielectric Loss of Triboelectric Layers for High-Performance Wearable Electricity Generation

High relative permittivity and low dielectric loss are two desired parameters of a triboelectric layer to enhance its mechanical-to-electrical energy conversion efficiency in a triboelectric nanogenerator (TENG). However, the elevated permittivity of the triboelectric layer is always accompanied by increasing dielectric loss, limiting further improvement or even reducing the electrical output. Herein, we report a method for improving the relative permittivity and suppressing the dielectric loss of the triboelectric layer via nanoscale design at the particle-polymer interface. When incorporated with 2 wt % Ag@C, the triboelectric-layer-enhanced TENG (TLE-TENG) presents a 2.6-fold increment in relative permittivity and a 302% current enhancement. An instantaneous peak power density of 1.22 W m^-2, an excellent pressure sensitivity of 90.95 V kPa^-1, and an optimized sheet resistance (∼0.14 Ω/sq) are attributes of this greatly enhanced device. Such improvements bode well for the implementation of these enhancing strategies to help position TLE-TENGs as pervasive and sustainable power sources and active self-powered sensors in the era of the Internet of Things.

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.

Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions

Advancements of virtual reality technology pave the way for developing wearable devices to enable somatosensory sensation, which can bring more comprehensive perception and feedback in the metaverse-based virtual society. Here, we propose augmented tactile-perception and haptic-feedback rings with multimodal sensing and feedback capabilities. This highly integrated ring consists of triboelectric and pyroelectric sensors for tactile and temperature perception, and vibrators and nichrome heaters for vibro- and thermo-haptic feedback. All these components integrated on the ring can be directly driven by a custom wireless platform of low power consumption for wearable/portable scenarios. With voltage integration processing, high-resolution continuous finger motion tracking is achieved via the triboelectric tactile sensor, which also contributes to superior performance in gesture/object recognition with artificial intelligence analysis. By fusing the multimodal sensing and feedback functions, an interactive metaverse platform with cross-space perception capability is successfully achieved, giving people a face-to-face like immersive virtual social experience.

Current wearable solutions for Virtual Reality (VR) have limitations of complicated structures and large driven power. Here, the authors report a highly integrated ring consisting of multimodal sensing and feedback units for augmented interactions in metaverse.

An Open-Environment Tactile Sensing System: Toward Simple and Efficient Material Identification

Robotic perception can have simple and effective sensing functions that are unreachable for humans using only the isolated tactile perception method, with the assistance of a triboelectric nanogenerator (TENG). However, the reliability of triboelectric sensors remains a major challenge due to the inherent environmental limitations. Here, an intelligent tactile sensing system that combines a TENG and deep-learning technology is proposed. Using a triboelectric triple tactile sensor array, typical characteristics of each testing material can be maintained stably even under different contact conditions (touch conditions and external environmental conditions) by extracting features from three independent electrical signals as well as the normalized output signals. Furthermore, a convolutional neural network model is integrated, and a high accuracy of 96.62% is achieved in a material identification task. The tactile sensing system is exhibited to an open environment for material identification and the real-time demonstration. Compared to the complex process that humans must integrate multiple sensing (touching and viewing) to accomplish tactile perception, the proposed sensing system shows a huge advantage in cognitive learning for the visually impaired, biomimetic prosthetics, and virtual spaces construction.

Sustainable Smart Industry: A Secure and Energy Efficient Consensus Mechanism for Artificial Intelligence Enabled Industrial Internet of Things

In recent years, the Internet of Things (IoT) has been industrializing in various real-world applications, including smart industry and smart grids, to make human existence more reliable. An overwhelming volume of sensing data is produced from numerous sensor devices as the Industrial IoT (IIoT) becomes more industrialized. Artificial Intelligence (AI) plays a vital part in big data analyses as a powerful analytic tool that provides flexible and reliable information insights in real-time. However, there are some difficulties in designing and developing a useful big data analysis tool using machine learning, such as a centralized approach, security, privacy, resource limitations, and a lack of sufficient training data. On the other hand, Blockchain promotes a decentralized architecture for IIoT applications. It encourages the secure data exchange and resources among the various nodes of the IoT network, removing centralized control and overcoming the industry's current challenges. Our proposed approach goal is to design and implement a consensus mechanism that incorporates Blockchain and AI to allow successful big data analysis. This work presents an improved Delegated Proof of Stake (DPoS) algorithm-based IIoT network that combines Blockchain and AI for real-time data transmission. To accelerate IIoT block generation, nodes use an improved DPoS to reach a consensus for selecting delegates and store block information in the trading node. The proposed approach is evaluated regarding energy consumption and transaction efficiency compared with the exciting consensus mechanism. The evaluation results reveal that the proposed consensus algorithm reduces energy consumption and addresses current security issues.

Deep Learning-Assisted Triboelectric Smart Mats for Personnel Comprehensive Monitoring toward Maritime Safety

Monitoring the crew of a ship can be performed by combining sensors and artificial intelligence methods to process sensing data. In this study, we developed a deep learning (DL)-assisted minimalist structure triboelectric smart mat system for obtaining abundant crew information without the privacy concerns of taking video. The smart mat system is fabricated using a conductive sponge with different filling rates and a fluorinated ethylene propylene membrane. The proposed dual-channel measurement method improves the stability of the generated signal. Comprehensive crew and cargo monitoring, including personnel and status identification, as well as positioning and counting functions are realized by the DL-assisted triboelectric smart mat system according to the analysis of instant sensory data. Real-time monitoring of crews through fixed and mobile devices improves the ability and efficiency of handling emergencies. The smart mat system provides privacy concerns and an effective way to build ship Internet of Things and ensure personnel safety.

Progress of Advanced Devices and Internet of Things Systems as Enabling Technologies for Smart Homes and Health Care

In the Internet of Things (IoT) era, various devices (e.g., sensors, actuators, energy harvesters, etc.) and systems have been developed toward the realization of smart homes/buildings and personal health care. These advanced devices can be categorized into ambient devices and wearable devices based on their usage scenarios, to enable motion tracking, health monitoring, daily care, home automation, fall detection, intelligent interaction, assistance, living convenience, and security in smart homes. With the rapidly increasing number of such advanced devices and IoT systems, achieving fully self-sustained and multimodal intelligent systems is becoming more and more important to realize a sustainable and all-in-one smart home platform. Hence, in this Review, we systematically present the recent progress of the development of advanced materials, fabrication techniques, devices, and systems for enabling smart home and health care applications. First, advanced polymer, fiber, and fabric materials as well as their respective fabrication techniques for large-scale manufacturing are discussed. After that, functional devices classified into ambient devices (at home ambiance such as door, floor, table, chair, bed, toilet, window, wall, etc.) and wearable devices (on body parts such as finger, wrist, arm, throat, face, back, etc.) are presented for diverse monitoring and auxiliary applications. Next, the current developments of self-sustained systems and intelligent systems are reviewed in detail, indicating two promising research directions in this field. Last, conclusions and outlook pinpointed on the existing challenges and opportunities are provided for the research community to consider.