Search Results (126)

Human sitting posture recognition is critical for smart seating, ergonomic monitoring, and healthcare systems. However, existing deep learning approaches typically rely on highly complex network architectures that are computationally expensive, hindering their lightweight deployment on edge devices. Furthermore, current methods frequently struggle with indistinct boundaries among multi-class postures and are highly prone to overfitting when constrained by small-sample pressure sensor datasets. To bridge this gap, this paper proposes a novel, lightweight posture recognition framework specifically tailored for pressure distribution maps. First, sitting pressure data is collected using a thin-film pressure array sensor and uniformly mapped into an [M × N] image representation, establishing an effective sample format for Convolutional Neural Network (CNN) inputs. Second, as our primary architectural contribution, we fundamentally optimize the classic LeNet-5 network to enhance complex feature representation without inflating model complexity. Specifically, the depth of the convolutional layers is increased with a progressively increasing channel configuration. Batch Normalization (BN) is introduced to accelerate convergence and ensure training stability, while a Dropout mechanism is embedded within the fully connected layers to strictly penalize overfitting under small-sample constraints. These architectural improvements are synergistically combined with targeted data augmentation strategies—including random translation, rotation, and intensity perturbation—to further strengthen the model’s generalization capability. Experimental results demonstrate that the proposed method achieves a classification accuracy of 95.5% in a five-class sitting posture recognition task, significantly outperforming baseline models such as the traditional LeNet-5, AlexNet-Lite, and VGG-Small. The findings indicate that this approach achieves an optimal balance among recognition accuracy, training stability, and low model complexity, providing a robust algorithmic baseline and proof-of-concept for smart healthcare perception systems, paving the way for future large-scale subject-independent validation. Full article

Localized surface plasmon resonance (LSPR) is an optical phenomenon that occurs when light interacts with free electrons on the surface of metallic thin films, producing intensified electromagnetic fields at specific sites, often called “hot spots”. LSPR-based sensing technologies respond to chemical and associated optical interfacial changes. Inherent advantages include enhanced sensitivity, compact size, low production cost, and strong potential for integration into portable, point-of-care diagnostic systems. This study focuses on a detailed investigation into the surface functionalization of localized surface plasmon resonance (LSPR)-based nanohole array (NHA) sensors for biomedical applications. Gold-coated NHA surfaces were functionalized using polyethylene glycol (PEG) self-assembled monolayers (SAMs), enabling specific attachment of biomolecular species. As a proof-of-concept, bovine serum albumin (BSA) and SARS-CoV-2 nanobody proteins were successfully immobilized on the PEGylated surfaces. Individual steps of surface modification including PEGylation, protein immobilization and nanobody immobilization were validated through a dual-method approach which combined measurement of LSPR optical spectral shifts and x-ray photoelectron spectroscopy (XPS) chemical analyses. Reproducibility was assessed across multiple sensors and repeated trials, confirming the repeatability of each functionalization and binding process. The sensor system, consisting of NHA-based plasmonic platform, microfluidics, and a portable optical spectrometer, exhibits the capability for reliable and sensitive, label-free detection of biomolecular targets, including viral antigens, in liquid-phase environments. Full article

To enhance immersion in virtual reality (VR) environments and improve the fidelity of virtual tactile interaction, this study proposes a perceptually grounded haptic-rendering framework for fine surface-texture simulation. The framework is centred on a Perceptual Haptic Spectrum Model (PHSM), which maps virtual surface attributes, including hardness, elasticity, roughness, friction, and microtexture periodicity, to multi-band tactile targets in perceptual frequency space. A Just Noticeable Difference (JND)-inspired parameterisation strategy is used as a design guideline to avoid imperceptible or redundant actuation, while region-specific response functions adapt the output to the fingertip centre, finger pad, and lateral edge. To improve reproducibility, the revised manuscript now specifies the flexible thin-film force/strain-sensor cell, array quantity, 320 Hz per-cell acquisition setting, signal-conditioning pipeline, contact-state classification rules, delay budget, and dual-actuation scheduling logic. The sensing design is based on a commercial flexible piezoresistive force-sensor cell with microsecond-level response time and a 12-bit ADC acquisition chain that provides a sufficient aggregate sampling margin for a 7–21 cell array. Manufacturer-supported sensor performance and prototype-level acceptance criteria are reported for response time, linearity, repeatability, hysteresis, drift, SNR, contact-state detection, latency, and durability. The system remains a proof-of-concept platform rather than a completed large-scale psychophysical validation. Within these boundaries, the results show coherent integration of perceptual modelling, multi-rate sensing, state monitoring, predictive feedforward control, and coordinated haptic actuation for fine VR texture rendering. Full article

Surface Acoustic Wave (SAW) magnetic sensors are traditionally fabricated on rigid substrates, which severely limits their application on curved or irregular surfaces. To address this critical limitation, this paper presents a novel flexible SAW magnetic sensor based on a grating-arrayed Terfenol-D thin film deposited on a 50 µm thick flexible lithium niobate (LiNbO₃) substrate. Unlike conventional designs using a continuous magnetostrictive layer, the proposed grating-arrayed structure is designed to aid in hysteresis compensation and minimize measurement errors associated with residual magnetization. As demonstrated experimentally, the sensors achieve a high sensitivity of 85.8 kHz/mT for devices with λ-wide gratings and a maximum frequency shift of 377 kHz at 5 mT. A systematic investigation reveals that sensitivity is critically dependent on the grating width and film thickness, with 500 nm thick gratings yielding optimal performance. Crucially, the sensor’s functionality under mechanical deformation is validated, and a differential measurement method is introduced to effectively compensate for stress-induced frequency shifts, ensuring reliable operation in practical, non-ideal conditions. The results confirm the sensor’s robust performance under the tested stress conditions, positioning this flexible SAW magnetic sensor as a promising solution for advanced, conformable sensing applications. Full article

Voice disorders severely limit verbal communication, creating a need for intuitive assistive technologies. To meet this need, we present epidermal strain sensors that capture strain signals during silent speech and hand gesture. A thin electrospun nanofiber layer integrated onto commercial polyurethane films guides uniform, controlled microcrack formation in screen-printed carbon conductive paths, achieving a gauge factor up to 243 over 0–40% strain. Signals from the seven-channel strain sensor array are recognized by a hybrid neural network that combines convolutional and Transformer architectures, reaching over 98% accuracy. The recognized outputs are rendered in virtual reality (VR), enabling intuitive, real-time communication. Moreover, the approach simplifies fabrication by enabling crack-based strain sensing with only a thin electrospun surface layer on commercial polyurethane films, eliminating the need for thick freestanding electrospun substrates. This cost-effective approach addresses limitations of conventional electrospun substrates by minimizing the thickness of the electrospun layer, thereby shortening the electrospinning time. Overall, the work demonstrates a method for translating natural non-verbal expressions into speech and text in VR, with promising applications in healthcare and assistive communication. Full article

Multi-point array thin-film thermocouples have strong potential for high-precision, wide-range temperature monitoring in applications such as aircraft engine thermal condition assessment and industrial process control. However, conventional single-point thin-film thermocouples cannot satisfy the distributed measurement requirements of large-area temperature fields, and the accuracy of multi-point arrays is often degraded by coupling effects among sensing nodes, which hinders their engineering deployment. In this work, a multi-point array thin-film thermocouple is fabricated via precision welding, and an insulating layer is deposited on the sensor surface using electrospray atomization to establish a multi-point temperature-sensing hardware system. To compensate for coupling-induced deviations, a deep learning–based calibration method is developed: measurements from the array and reference thermocouples are synchronously collected to build the dataset, outliers are removed using the interquartile range (IQR) method, and a three-hidden-layer multilayer perceptron (MLP) is trained for each node independently using the Adam optimizer (learning rate 0.001) with an 8:2 train–test split. Performance is quantified by MAE, MSE, and R², and the results show that the proposed approach markedly reduces measurement errors and improves the accuracy of the array thermocouples, demonstrating reliable performance and practical applicability for precise large-area temperature-field monitoring. Full article

Biocompatible thin films are essential for advancing biomedical devices, as they enhance integration with biological tissues, improve device longevity, and reduce complications. The rapid evolution of both medical needs and materials science has led to a diverse array of deposition techniques, each offering unique advantages and challenges for tailoring surface properties without compromising the bulk characteristics of implants and sensors. While laser-based methods—such as pulsed laser deposition (PLD) and Matrix-Assisted Pulsed Laser Evaporation (MAPLE)—are renowned for their precision, ability to preserve complex material stoichiometry, and suitability for low-temperature processing, the broader landscape includes several other important approaches. Physical Vapor Deposition (PVD) techniques, including magnetron sputtering and pulsed electron deposition, are widely used for their ability to create uniform, adherent coatings with controlled thickness and composition, making them suitable for both hard and soft biomedical substrates. Chemical Vapor Deposition (CVD) and its plasma-enhanced variant (PECVD) offer conformal coatings and excellent control over film chemistry, which is particularly valuable for functional polymer and ceramic films. Other methods, such as sol–gel processing, ion beam deposition, and electrophoretic deposition, provide additional flexibility in terms of coating composition, adhesion, and processing temperature, allowing for the fabrication of films with tailored mechanical, chemical, and biological properties. Despite these advances, the field faces ongoing challenges in optimizing film properties for specific clinical applications, ensuring reproducibility, and scaling up production for widespread use. The necessity of this review lies in its comprehensive comparison of laser-based techniques with alternative deposition methods, providing critical insights into their respective strengths, limitations, and suitability for different biomedical scenarios. By synthesizing recent developments and highlighting current gaps, this review aims to guide researchers and clinicians in selecting the most appropriate thin-film deposition strategies to meet the evolving demands of next-generation biomedical devices. Full article

Hydrogenated amorphous silicon (a-Si:H) is a mature thin-film technology for large-area devices and thin-film sensors, and its low-temperature growth via Plasma-Enhanced Chemical Vapor Deposition (PECVD) makes it particularly suitable for biomedical flexible and wearable platforms. However, the reliable integration of a-Si:H sensors on polymer substrates requires a quantitative assessment of their electrical stability under mechanical stress, since bending-induced variations may affect sensor accuracy. In this work, we provide a quantitative, direction-dependent evaluation of the static-bending robustness of both single-doped a-Si:H layers and complete p-i-n junction stacks on polyimide (Kapton^®), thereby linking material-level strain sensitivity to device-level functionality. First, n- and p-doped a-Si:H layers were deposited on 50 µm thick Kapton^® and then structured as two-terminal thin-film resistors to enable resistivity extraction under bending conditions. Electrical measurements were performed on multiple samples, with the current path oriented either parallel (longitudinal) or perpendicular (transverse) to the bending axis, and resistance profiles were determined as a function of bending radius. While n-type layers exhibited limited and mostly gradual variations, p-type layers showed a stronger sensitivity to mechanical stress, with a critical-radius behavior under transverse bending and a more progressive evolution in the longitudinal one. This directional response identifies a practical bending condition under which doped layers, particularly p-type films, are more susceptible to strain-induced degradation. Subsequently, a linear array of a-Si:H p-i-n sensors was fabricated on Kapton^® substrates with two different thicknesses (25 and 50 µm thick) and characterized under identical bending conditions. Despite the increased strain sensitivity observed in the single-layers, the p-i-n diodes preserved their rectifying behavior down to the smallest radius tested. Indeed, across the investigated radii, the reverse current at −0.5 V remained consistent, confirming stable junction operation under bending. Only minor differences, related to substrate thickness, were observed in the reverse current and in the high-injection regime. Overall, these results demonstrate the mechanical robustness of stacked a-Si:H junctions on polyimide and support their use as sensors for wearable biosensing architectures. By establishing a quantitative, orientation-aware stability benchmark under static bending, this study supports the design of reliable a-Si:H flexible sensor platforms for curved and wearable surfaces. Full article

Developing flexible, cost-effective, and durable sensors is a key challenge for integrating Cyber–Physical–Human Systems (CPHSs) into smart homes. This paper introduces a flexible pressure sensor array designed for CPHS applications, addressing the need for cost-effective and durable sensors in smart homes. Our approach combines flexible piezoelectric materials with Swept Frequency Capacitive Sensing (SFCS). Unlike previous pressure sensors made of flexible piezoelectric materials, which can only measure dynamic pressure due to charge leakage, by using SFCS, the piezoelectric material is not directly in the circuit, and our sensor can effectively measure static pressure. While traditional arrays require multiple I/O ports or a matrix configuration, our design measures four distinct locations using only a single I/O port. The sensor is also mechanically flexible and exhibits high durability, capable of functioning even after being cut or torn, provided the electrode contact area remains largely intact. To decode the complex, multiplexed signal from this single channel, we developed a two-stage deep learning pipeline. We utilized data from thin-film resistive pressure sensors as ground truth. A classification model determines which of the four sensors are being touched. Then a regression model uses this touch-state information to estimate the corresponding pressure values. This pipeline employs a hybrid architecture that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The results show that the system can estimate pressure values at each location. To demonstrate its application, the sensor system was integrated into a power recliner, thereby transforming the chair into an interactive tool for daily exercise designed to improve the well-being of older adults. This successful implementation establishes a viable pathway for the development of intelligent, interactive furniture for in-home exercise and rehabilitation within the CPHS paradigm. Full article

Poly(3-thienylboronic acid) (PThBA) has recently been suggested as a conducting polymer with affinity for saccharides. In this study, thin films of this compound were deposited onto gold electrodes. The system obtained was studied as a possible chemical sensor. The measurements were performed by impedance spectroscopy using potassium ferro/ferricyanide as a redox mediator. The thickness of the polymer and the deposition of the adhesive sublayer were optimized to achieve a compromise between the blocking of defects in the polymer layer and the unnecessary increase in the internal resistance of this conductometric sensor. A comparative study of the influence of fructose, glucose, and sorbitol on transversal polymer resistance was conducted. The binding constants for these saccharides were extracted from the concentration dependencies of sensor conductance. Among them, sorbitol showed the highest affinity with a binding constant up to ~15,000 L·mol⁻¹, followed by fructose (~8700 L·mol⁻¹) and glucose (~4500 L·mol⁻¹). In order to exclude the contribution of the analyte tautomers on the obtained binding constants, measurements of ethylene glycol were also performed. The effects of pH and the redox state of PThBA on its affinity properties were studied, revealing higher affinities at alkaline pH and in oxidized state of the chemosensitive polymer. The developed system has the capacity to be applied in chemical sensors and virtual sensor arrays with electrical affinity control. Full article

The integration of a pixelated Fabry–Pérot filter array onto the image sensor enables on-chip snapshot multispectral imaging, significantly reducing the size and weight of conventional spectral imaging equipment. However, a traditional Fabry–Pérot cavity, based on metallic or dielectric layers, exhibits a narrow bandwidth, which restricts their utility in broader applications. In this work, we propose novel Fabry–Pérot filter structures that employ dielectric thin films for phase modulation, enabling single-peak filtering across a broad operational wavelength range from 400 nm to 1100 nm. The proposed structures are easy to fabricate and compatible with complementary metal-oxide-semiconductor (CMOS) image sensors. Moreover, the structures show low sensitivity to oblique incident angles of up to 30° with minimal wavelength shifts. This advanced Fabry–Pérot filter design provides a promising pathway for expanding the operational wavelength of snapshot spectral imaging systems, thereby potentially extending their application across numerous related fields. Full article

Conventional static fixation for distal radius fractures (DRF) is clinically challenging, with methods often leading to complications such as malunion and pressure-related injuries. These issues stem from uncontrolled pressure and a lack of real-time biomechanical feedback, resulting in suboptimal functional recovery. To overcome these limitations, we engineered an intelligent, adaptive orthopedic device. The system is built on a patient-specific, 3D-printed architecture for a lightweight, personalized fit. It embeds an array of thin-film pressure sensors at critical anatomical sites to continuously quantify biomechanical forces. This data is transmitted via an Internet of Things (IoT) module to a cloud platform, enabling real-time remote monitoring by clinicians. The core innovation is a closed-loop feedback controller governed by a robust Interval Type-2 Fuzzy Logic (IT2-FLC) algorithm. This system autonomously adjusts servo-driven straps to dynamically regulate fixation pressure, adapting to changes in limb swelling. In a preliminary clinical evaluation, the group receiving the integrated treatment protocol, which included the smart splint and TCM herbal therapy, demonstrated superior anatomical restoration and functional recovery, evidenced by higher Cooney scores (91.65 vs. 83.15) and lower VAS pain scores. This proof-of-concept study validates a new paradigm for adaptive orthopedic devices, showing high potential for clinical translation. Full article

High-density tactile sensor arrays that replicate human touch could restore texture perception in paralyzed individuals. However, conventional tactile sensor arrays face inherent trade-offs between spatial resolution, sensitivity, and crosstalk suppression due to microstructure size limitations and signal interference. To address this, we developed a tactile sensor featuring 10 μm-scale pyramid tips that achieve ultra-high sensitivity (8.082 kPa⁻¹ in 0.2–0.5 kPa range). By integrating a flexible resistive sensing layer with a 256 × 256 active-matrix thin-film transistor (TFT) readout system, our design achieves 500 μm spatial resolution—surpassing human fingertip discrimination thresholds. The sensor demonstrates rapid response (125 ms), exceptional stability (>1000 cycles), and successful reconstruction of 500 μm textures and Braille patterns. This work establishes a scalable platform for high-fidelity tactile perception in static fine texture recognition. Full article

Point-of-care (POC) diagnostic technologies have become essential for the real-time monitoring and management of chronic wounds, where maintaining a moist environment and controlling pH levels are critical for effective healing. In this study, a flexible pH sensor based on a graphene/molybdenum disulfide (graphene/MoS₂) composite interdigitated electrode (IDE) structure was fabricated using pulsed laser ablation. The pH sensor, with an active area of 30 mm × 30 mm, exhibited good adhesion to the polyethylene terephthalate (PET) substrate and maintained structural integrity under repeated bending cycles. Precise ablation was achieved under optimized conditions of 4.35 J/cm² laser fluence, a repetition rate of 300 kHz, and a scanning speed of 500 mm/s, enabling the formation of defect-free IDE arrays without substrate damage. The influence of laser processing parameters on the surface morphology, electrical conductivity, and wettability of the composite thin films was systematically characterized. The fabricated pH sensor exhibited high sensitivity (~4.7% change in current per pH unit) across the pH 2–10 range, rapid response within ~5.2 s, and excellent mechanical stability under 100 bending cycles with negligible performance degradation. Moreover, the sensor retained > 95% of its stable sensitivity after 7 days of ambient storage. Furthermore, the pH response behavior was evaluated for electrode structures with different pitches, demonstrating that structural design parameters critically impact sensing performance. These results offer valuable insights into the scalable fabrication of flexible, wearable pH sensors, with promising applications in wound monitoring and personalized healthcare systems. Full article

Recently, with the development of automation technology in various fields, much research has been conducted on infrared photodetectors, which are the core technology of LiDAR sensors. However, most infrared photodetectors are expensive because they use compound semiconductors based on epitaxial processes, and they have low safety because they use the near-infrared (NIR) region that can damage the retina. Therefore, they are difficult to apply to automation technologies such as automobiles and factories where humans can be constantly exposed. In contrast, short-wavelength infrared photodetectors based on PbS QDs are actively being developed because they can absorb infrared rays in the eye-safe region by controlling the particle size of QDs and can be easily and inexpensively manufactured through a solution process. However, PbS QDs-based SWIR photodetectors have low chemical stability due to the electron/hole extraction layer processed by the solution process, making it difficult to manufacture them in the form of patterning and arrays. In this study, bulk NiO and ZnO were deposited by sputtering to achieve uniformity and patterning of thin films, and the performance of PbS QDs-based photodetectors was improved by optimizing the thickness and annealing conditions of the thin films. The fabricated photodetector achieved a high response characteristic of 114.3% through optimized band gap and improved transmittance characteristics. Full article