Sign language recognition, especially the sentence recognition, is of great significance for lowering the communication barrier between the hearing/speech impaired and the non-signers. The general ...glove solutions, which are employed to detect motions of our dexterous hands, only achieve recognizing discrete single gestures (i.e., numbers, letters, or words) instead of sentences, far from satisfying the meet of the signers' daily communication. Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface. Non-segmentation and segmentation assisted deep learning model achieves the recognition of 50 words and 20 sentences. Significantly, the segmentation approach splits entire sentence signals into word units. Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences. Furthermore, new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%. Finally, the sign language recognition results are projected into virtual space and translated into text and audio, allowing the remote and bidirectional communication between signers and non-signers.
Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors ...to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications.
Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the ...video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique "identity" electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security.
Chemotherapy is currently an important modality in the treatment of cancer and is widely used in the treatment of many cancers. However, the main problem with chemotherapy is the highly toxic side ...effects. How improve the efficacy of drugs and reduce their side effects has been a challenge for cancer treatment research. With the booming development of nanotechnology, functionalized nanomaterials are widely used for drug delivery and the treatment of tumors. Nanomaterials have unique physical, chemical, and optical properties, and the drug delivery system constructed by using multifunctional nanomaterials can not only precisely kill tumor cells to reduce damage to normal tissues, but also combine multiple therapeutic modalities simultaneously to improve the treatment effect of cancer. Therefore, the development of precise nano-targeted therapeutic techniques is of great importance for the treatment of cancer in clinical settings. In this thesis, we constructed various nano-drug delivery systems based on functionalized nanomaterials and applied them to drug delivery to tumors and multimodal therapy. These studies will help advance the development of nanomolecular drug delivery technologies to improve the therapeutic efficacy of drugs, reduce side effects and toxicity, and provide safer and more effective drug treatment options for clinical therapy.
The need of self-powered sensors and self-sustainable micro-/nano-systems is increasing for smart home applications. With the aid of the fifth-generation (5G) wireless communication and the ...artificial intelligence (AI) technology, numerous sensors can form an artificial intelligence of things (AIoT) system with a cloud computing server to collect, store, process, analyze data, and control the system. In this review, we briefly introduce the major sensor transducing mechanisms and the corresponding representative sensors. Then we emphasize the discussion of triboelectric sensors and their use as various types of human machine interfaces (HMIs). Regarding integrated self-sustainable systems with energy storage function for more general application scenarios, we present the recent advances of integration of triboelectric nanogenerator with supercapacitors, lithium-ion batteries, and solar cells. The development of materials and configurations with novel functionalities to improve the system performance are discussed. We present the state-of-the-art works based on the synergy between wearable sensors and AI technology, highlighting the system-level function of analyzing abundant sensory data, recognizing events, and making decisions accordingly. We wrap up the review by providing our perspectives of future development of self-powered sensors and self-sustainable micro-/nano-systems towards improved wearability, multifunctionality, better self-sustainability via multi-domain energy harvesting and storage technology integration, and higher-level intelligence for complex scenarios.
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•Evolution from self-powered sensors to self-sustainable micro-/nano-systems for 5G smart homes in the AIoT era was reviewed.•Recent advances of self-powered physical sensors, chemical sensors, and human-machine interfaces were presented.•System integration of TENGs and energy storages towards self-sustainable micro-/nano-systems was summarized.•Fusion of AIoT and self-sustainable micro-/nano-systems was highlighted.
Abstract
Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and ...bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks. In addition to intuitive methods like enhancement of electric field of PNAs and enrichment of molecules on PNAs surfaces, we propose a loss engineering method to optimize damping rate by reducing radiative loss using hook nanoantennas (HNAs). Furthermore, with the spectral multiplexing of the HNAs from gradient dimension, the wavelength-multiplexed HNAs (WMHNAs) serve as ultrasensitive vibrational probes in a continuous ultra-broadband region (wavelengths from 6 μm to 9 μm). Leveraging the multi-dimensional features captured by WMHNA, we develop a machine learning method to extract complementary physical and chemical information from molecules. The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs. Our work brings another degree of freedom to optimize PNAs towards small-volume, real-time, label-free molecular recognition from various species in low concentrations for chemical and biological diagnostics.
The past few years have witnessed the significant impacts of wearable electronics/photonics on various aspects of our daily life, for example, healthcare monitoring and treatment, ambient monitoring, ...soft robotics, prosthetics, flexible display, communication, human‐machine interactions, and so on. According to the development in recent years, the next‐generation wearable electronics and photonics are advancing rapidly toward the era of artificial intelligence (AI) and internet of things (IoT), to achieve a higher level of comfort, convenience, connection, and intelligence. Herein, this review provides an opportune overview of the recent progress in wearable electronics, photonics, and systems, in terms of emerging materials, transducing mechanisms, structural configurations, applications, and their further integration with other technologies. First, development of general wearable electronics and photonics is summarized for the applications of physical sensing, chemical sensing, human‐machine interaction, display, communication, and so on. Then self‐sustainable wearable electronics/photonics and systems are discussed based on system integration with energy harvesting and storage technologies. Next, technology fusion of wearable systems and AI is reviewed, showing the emergence and rapid development of intelligent/smart systems. In the last section of this review, perspectives about the future development trends of the next‐generation wearable electronics/photonics are provided, that is, toward multifunctional, self‐sustainable, and intelligent wearable systems in the AI/IoT era.
Wearable electronics and photonics have advanced rapidly and impacted significantly on the way of healthcare monitoring/treatment, ambient monitoring/intervention, prosthetics, soft robotics, flexible display, communication, human‐machine interactions, and so on. This review summarizes the recent progress of wearable electronics/photonics in the aspects of emerging materials, transducing mechanisms, structural configurations, technology fusion, and practical applications toward the artificial intelligence/internet of things era.
Abstract
The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal ...information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.
Microplastics can have strong sorption capacity for many contaminants, thus greatly influencing the fate, transport and bioavailability of those contaminants in the environment. However, the effect ...of particle size on contaminant sorption by microplastics is still poorly understood. This study investigated the sorption of phenanthrene and nitrobenzene to micron-, submicron- and nano- sized polystyrene microplastics of 170 µm, 102 µm, 50 µm, 30 µm, 800 nm, 235 nm or 50 nm. All phenanthrene sorption isotherms and most nitrobenzene sorption isotherms were linear because of the strong sorption capacity of microplastics and the hydrophobic partitioning. The log Kd values ranged between 3.07–4.20 and 1.58–3.14 log (L/kg) for phenanthrene and nitrobenzene, respectively. The log Kd values of phenanthrene and nitrobenzene both increased with decreasing particle size for micron-sized polystyrenes (micro-polystyrene) and submicron-sized polystyrenes (submicro-polystyrene). However, in comparison with 235 nm submicro-polystyrene, the log Kd values of 50 nm nano-polystyrene were significantly lower for phenanthrene and comparable for nitrobenzene because its aggregation greatly reduced the effective surface area accessible for sorption. The results improved our understanding of the fate and risks of microplastics associated with the two typical organic contaminants in the micrometer to nanometer scale.
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•Size effect of microplastics on the sorption of contaminants was studied.•Role of polarity in phenanthrene and nitrobenzene sorption was revealed.•Sorption behaviors were altered when microplastics size reduced to nanometer.•Lower sorption capacity was due to the aggregation of nano-sized microplastics.
The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as ...an intuitive and low‐cost HMI can expediently track the motions of human fingers, resulting in a straightforward communication media of human–machine interactions. When combining several triboelectric textile sensors and proper machine learning technique, it has great potential to realize complex gesture recognition with the minimalist‐designed glove for the comprehensive control in both real and virtual space. However, humidity or sweat may negatively affect the triboelectric output as well as the textile itself. Hence, in this work, a facile carbon nanotubes/thermoplastic elastomer (CNTs/TPE) coating approach is investigated in detail to achieve superhydrophobicity of the triboelectric textile for performance improvement. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low‐cost and self‐powered interface for gesture recognition. By leveraging machine learning technology, various gesture recognition tasks are done in real time by using gestures to achieve highly accurate virtual reality/augmented reality (VR/AR) controls including gun shooting, baseball pitching, and flower arrangement, with minimized effect from sweat during operation.
With capabilities of humidity‐resistant and anti‐sweat, high‐accuracy complicated gesture recognition based on machine learning is realized using a low‐cost and self‐powered superhydrophobic glove human machine interface (HMI) with minimized sweat effect. With gesture recognition, the virtual reality/augmented reality (VR/AR) controls including shooting, baseball pitching, and floral arrangement are successfully demonstrated.
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