Optical Microfiber Intelligent Sensor: Wearable Cardiorespiratory and Behavior Monitoring with a Flexible Wave-Shaped Polymer Optical Microfiber

Electrically Driven PANI-Based Multilayer Nanocomposite Coatings for Dynamic Color Modulation

Reconfigurable precision regulation of structural color devices is a critical step toward versatile advanced functionality and broadening application scenarios. In this work, we demonstrate the precise manipulation of red, green, and blue, three primary colors generated by the five-layer nanocomposite coatings composed of polyaniline/indium tin oxide/titanium/titanium dioxide/titanium (PANI/ITO/Ti/TiO2/Ti). The modulation of dynamic structural colors of the proposed multilayer coatings originates from a cooperative interaction between the top PANI conductive polymer and the bottom traditional four-layer structural color consisting of ITO/Ti/TiO2/Ti. Specifically, the three primitive colors are first achieved by three different combinations of both TiO2 and ITO thicknesses, and then each color is modulated precisely by an intrinsic color change of the top PANI conductive layer at various voltages. As a result, the nanocomposite coatings demonstrate millisecond response time, excellent durability over 100 cycles, driving voltages below 1 V, and a wide temperature tolerance range from 5 to 60 °C. Additionally, centimeter-scale R, G, and B letter-shaped samples are fabricated using a mask plate coating technique, showcasing color modulation in patterned samples. Our work offers a straightforward strategy to realize the dynamic manipulation of the three primary colors within a certain range, which lays a solid foundation for the development of dynamic display technologies, such as dynamic paintings and e-books.

Wearable Photonic Artificial Throat for Silent Communication and Speech Recognition

Advocating for the voices of the disabled, particularly through wearable artificial throats, has garnered significant attention recently. Such devices necessitate sensors with stretchability, high sensitivity, and excellent skin conformability. In this study, an intelligent photonic artificial throat has been developed. It features a sandwich-structured optical fiber sensor encapsulated in Dragon Skin 20, which has an elastic modulus similar to human tissue and is integrated with sensitivity-enhancing rings and fabric for enhanced wearability. With ultrafast response (response time: 10 ms, recovery time: 32 ms) and high sensitivity (1.92 μW/mN), it detects throat area vibrations and muscle contractions, accurately identifying tones in Mandarin, vowels, words, and sentences in English, achieving accurate bilingual detection. It also distinguishes animal sounds (horse neighing and cuckoo's call) and pop songs when mounted on speakers. Furthermore, the artificial throat can accurately detect subtle movements of the head and neck, and by combining nodding actions with the MORSE code, silent communication between individuals has been successfully achieved. Integrated with an advanced artificial intelligence (AI) algorithm, it recognizes tones (97.50%), vowel letters (97.00%), common words (98.00%) and sentences (96.52%), opening prospects for biomedical applications, language education, speech recognition, motion monitoring, and more.

Patterning Adhesive Layers for Array Electrodes via Electrochemically Grafted Polymers

Electrophysiological sensors (electrodes) are used to collect complex electrophysiological signals, providing extensive information about the body's condition. Reliable signal acquisition necessitates stable skin-electrode interfaces to prevent adverse effects arising from interface variations. Although the incorporation of conductive adhesive layers can improve the stability of these interfaces, in array electrodes, the layer may also cause short circuits and signal crosstalk. Here, we propose a general strategy for patterning the adhesive layer of array electrodes based on electrochemically grafted adhesive polymers (EGAPs). Utilizing the conductivity differences between the sensing sites and the substrate material of flexible electrodes, spatial selective loading of adhesive and ionically conductive polymers can be achieved through in situ electrochemical reactions, thus realizing spontaneous patterning. This EGAP-based method allows for a rapid and selective electrode surface modification in just two steps. Furthermore, array electrodes with EGAP acquired stable electrophysiological signals while improving the stability of the skin-electrode interface and the quality of signal collected and effectively avoided signal crosstalk between arrayed sensing sites.

Optical tactile sensor based on a flexible optical fiber ring resonator for intelligent braille recognition

Inspired by human skin, bionic tactile sensing is effectively promoting development and innovation in many fields with its flexible and efficient perception capabilities. Optical fiber, with its ability to perceive and transmit information and its flexible characteristics, is considered a promising solution in the field of tactile bionics. In this work, one optical fiber tactile sensing system based on a flexible PDMS-embedded optical fiber ring resonator (FRR) is designed for braille recognition, and the Pound-Drever-Hall (PDH) demodulation scheme is adopted to improve the detection sensitivity. Theoretical simulations and experimental verifications show that by adopting a bionic sliding approach and a Multilayer Perceptron Neural Network, a single FRR with a hardness gradient design can detect eight different tactile pressures in braille characters with an accuracy of 98.57%. Furthermore, after training and testing, the MLP-LSTM model classifies time series signals, thereby achieving completely accurate encoding of braille keywords and braille poems. The advantages of the optical fiber tactile sensing system in this study are that the high-quality factor FRR can detect subtle differences in braille dots, it is not affected by changes in optical power due to its relies on PDH frequency demodulation, and the application of machine learning algorithms can enhance the robustness to slight pressure errors and simplify the recognition process. This solution opens up what we believe is a new optical approach for bionic tactile perception and has important potential value in promoting human-computer interaction, smart medical care, and other fields.

Fabrication of Flexible Wearable Mechanosensors Utilizing Piezoelectric Hydrogels Mechanically Enhanced by Dipole-Dipole Interactions

Conductive hydrogels have been increasingly employed to construct wearable mechanosensors due to their excellent mechanical flexibility close to that of soft tissues. In this work, piezoelectric hydrogels are prepared through free radical copolymerization of acrylamide (AM) and acrylonitrile (AN) and further utilized in assembling flexible wearable mechanosensors. Introduction of the polyacrylonitrile (PAN) component in the copolymers endows the hydrogels with excellent piezoelectric properties. Meanwhile, significant enhancement of mechanical properties has been accessed by forming dipole-dipole interactions, which results in a tensile strength of 0.51 MPa. Flexible wearable mechanosensors are fabricated by utilizing piezoelectric hydrogels as key signal converting materials. Self-powered piezoelectric pressure sensors are assembled with a sensitivity (S) of 0.2 V kPa-1. Additionally, resistive strain sensors (gauge factor (GF): 0.84, strain range: 0-250%) and capacitive pressure sensors (S: 0.23 kPa-1, pressure range: 0-8 kPa) are fabricated by utilizing such hydrogels. These flexible wearable mechanosensors can monitor diverse body movements such as joint bending, walking, running, and stair climbing. This work is anticipated to offer promising soft materials for efficient mechanical-to-electrical signal conversion and provides new insights into the development of various wearable mechanosensors.

Twistable and Stretchable Nasal Patch for Monitoring Sleep-Related Breathing Disorders Based on a Stacking Ensemble Learning Model

Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces learning ability and memory. The existing polysomnography used to measure sleep disorders is executed in an unfamiliar environment, which may result in sleep patterns that are different from usual, reducing accuracy. This study reports a machine learning-based personalized twistable patch system that can simply measure obstructive sleep apnea syndrome in daily life. The stretchable patch attaches directly to the nose in an integrated form factor, detecting sleep-disordered breathing by simultaneously sensing microscopic vibrations and airflow in the nasal cavity and paranasal sinuses. The highly sensitive multichannel patch, which can detect airflow at the level of 0.1 m/s, has flexibility via a unique slit pattern and fabric layer. It has linearity with an R2 of 0.992 in the case of the amount of change according to its curvature. The stacking ensemble learning model predicted the degree of sleep-disordered breathing with an accuracy of 92.9%. The approach demonstrates high sleep disorder detection capacity and proactive visual notification. It is expected to help as a diagnostic platform for the early detection of chronic diseases such as cerebrovascular disease and diabetes.

AI-Assisted Insole Sensing System for Multifunctional Plantar-Healthcare Applications

The pervasive global issue of population aging has led to a growing demand for health monitoring, while the advent of electronic wearable devices has greatly alleviated the strain on the industry. However, these devices come with inherent limitations, such as electromagnetic radiation, complex structures, and high prices. Herein, a Solaris silicone rubber-integrated PMMA polymer optical fiber (S-POF) intelligent insole sensing system has been developed for remote, portable, cost-effective, and real-time gait monitoring. The system is capable of sensitively converting the pressure of key points on the sole into changes in light intensity with correlation coefficients of 0.995, 0.952, and 0.910. The S-POF sensing structure demonstrates excellent durability with a 4.8% variation in output after 10,000 cycles and provides stable feedback for bending angles. It also exhibits water resistance and temperature resistance within a certain range. Its multichannel multiplexing framework allows a smartphone to monitor multiple S-POF channels simultaneously, meeting the requirements of convenience for daily care. Also, the system can efficiently and accurately provide parameters such as pressure, step cadence, and pressure distribution, enabling the analysis of gait phases and patterns with errors of only 4.16% and 6.25% for the stance phase (STP) and the swing phase (SWP), respectively. Likewise, after comparing various AI models, an S-POF channel-based gait pattern recognition technique has been proposed with a high accuracy of up to 96.87%. Such experimental results demonstrate that the system is promising to further promote the development of rehabilitation and healthcare.

Advanced fabrication of polymer waveguide interferometric sensor utilizing interconnected holey fibers

A universally applicable approach is proposed for the fabrication of fiber-optic polymer sensors. The hollow-core fibers (HCFs) with inner diameters of 30 µm, 50 µm, and 75 µm are spliced coaxially with dual-hole fiber (DHF) or photonic crystal fiber (PCF). Owing to the sized-matched air holes within HCF and DHF/PCF, an interconnected in-fiber microchannel is constructed, which facilitates rapid and complete filling of the HCF's central hole with liquid glue. After the ultraviolet-induced polymerization, a polymer Fabry-Perot interferometer is achieved by cutting the HCF end with a desired cavity length. Besides, the interference visibility is significantly enhanced by adding a refractive-index-modulated polymer cap onto the cutting surface. Experimental results demonstrate the optimized interference spectra and the interconnection of the matched air-hole fibers. The polymer sensor exhibits a signal-to-noise ratio of 56.8 dB for detecting pulsed ultrasonic waves, which is more than twice that of a partially polymer-filled sensor. Due to the hermetically-sealed structure, the sensor probe presents constrained performance with a temperature sensitivity of 230.2 pm/°C and a humidity sensitivity of 93.7 pm/%RH, which can be further improved by releasing the polymer waveguide from fiber cladding. Based on interconnected holey fibers, the proposed approach has a uniform size-controlled polymer waveguide dimension with increased spectrum visibility, rendering it suitable for a diverse range of microstructure-matched optical fibers.

AI-Driven Sensing Technology: Review

Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.

A Wearable Sandwich Heterostructure Multimode Fiber Optic Microbend Sensor for Vital Signal Monitoring

This work proposes a highly sensitive sandwich heterostructure multimode optical fiber microbend sensor for heart rate (HR), respiratory rate (RR), and ballistocardiography (BCG) monitoring, which is fabricated by combining a sandwich heterostructure multimode fiber Mach-Zehnder interferometer (SHMF-MZI) with a microbend deformer. The parameters of the SHMF-MZI sensor and the microbend deformer were analyzed and optimized in detail, and then the new encapsulated method of the wearable device was put forward. The proposed wearable sensor could greatly enhance the response to the HR signal. The performances for HR, RR, and BCG monitoring were as good as those of the medically approved commercial monitors. The sensor has the advantages of high sensitivity, easy fabrication, and good stability, providing the potential for application in the field of daily supervision and health monitoring.