This paper introduces a wearable and portable computer interface designed to produce virtual taste sensations on the human tongue without the use of chemicals. The device achieves this by delivering electrical stimulation, and the intensity and type of taste sensations can be adjusted by modifying parameters such as frequency, duty cycle, and voltage intensity of the output signal. Our study demonstrates that this compact system can reliably evoke statistically significant sour and salty taste sensations, with over 50 participants also reporting experiences of spicy, bitter, metallic, electric, and pressure sensations. Increased voltage amplitude resulted in more intense sensations, while lower duty cycles produced lingering cold feelings, and higher duty cycles generated pressure and discomfort. Interestingly, the intensity of metallic taste sensations remained consistent across varying duty cycles and frequencies. This research highlights the potential of wearable digital taste technology to revolutionize virtual sensory experiences, opening new opportunities for applications in immersive media, health, and entertainment.

Wearing wearable devices has become a part of daily life for many people worldwide. Wearables provide various applications, including health monitoring, diagnosis, treatment, and rehabilitation. Since wearable devices are in close contact with the human body, considering human factors aspects during the design and development stages is essential for the success of future wearables. This research aims to assess the optimal body part for medical wearable sensors’ placement based on the wearable’s expert opinion questionnaire. The study focused on four main categories: cardiovascular monitoring, neuromuscular monitoring, biofluids, and gait disorders, considering three placement criteria: comfort, accuracy, and simplicity. Corresponding to the findings, there is a gap between the recent research outcomes and the experts’ answers. In particular, the answers to the questions related to the placements of wearable sensors for neurological and gait disorders were limited to the traditional clinical diagnosis techniques. This could be attributed to insufficient knowledge and collaboration between engineers and medical professionals. This highlights the need for a systematic way to evaluate the wearability of wearables based on their performance alongside wearability criteria by establishing a wearability assessment human-centric framework for each wearable sensor application supported by clinical studies and experimental data, which could reduce the time for development and commercialization of accurate, reliable, and comfortable wearable medical devices.

In order to solve the problems of low efficiency and poor accuracy in measuring the geometrical parameters of braided pipe mesh by traditional methods, an improved method for measuring the geometrical parameters of braided pipe is proposed. The method acquires the braided pipe grid image by a CMOS camera and reduces the influence of color and noise on the image quality by using preprocessing means such as grayscaling and bilateral filtering. Subsequently, the region of interest of the woven tube grid structure is extracted from the background using an iterative method, and the average filament diameter is calculated by Canny edge detection and a modified Hough transform. Next, the skeleton wire segment contours are obtained using the skeleton operator, and the contours are further optimized by the RDP algorithm to retain only the straight line segments. The co-linear straight line segments are connected to form a complete mesh line by a contour merging operation. Finally, the mesh size is indirectly obtained using the improved least-squares method based on feature classification. Through experimental verification, the method has a practical value as it improves the average wire diameter accuracy by 3% and the mesh size accuracy by 1% compared with the traditional machine vision measurement method.

Here in this work, a graphene-based absorber in THz waves is introduced. The structure exploits the mechanical adjusting setup using a Micro-Electro-Mechanical Systems comb driver. In addition, the periodic graphene rings and disks are used on top of the TOPAS (cyclic olefin copolymer) spacer. The middle layer spacer is considered as an air gap that can be filled by any target samples. The mechanical tuning besides the influence of the chemical potential makes the proposed structure most tunable with more than five absorption peaks in the THz gap. Two parallel simulation paths are followed in this work. First, an equivalent circuit model representation is developed, and the absorption response is obtained by leveraging the impedance matching theorem. Then the full-wave numerical modeling via the finite element method is performed to investigate the first approach’s validity and accuracy. It is shown that combining electrical and mechanical stimulations can change the number of absorption peaks and also their frequencies. According to the simulation results, the proposed absorber is highly reliable and robust against probable geometrical mismatches, while almost all of the THz spectrum is covered by the proposed structure.

Uncontrolled blood pressure poses significant health risks, making accurate measurement essential in healthcare. Conventional blood pressure measurement methods, typically using inflatable cuffs, can cause patient discomfort, tissue damage, and are unsuitable for long-term monitoring. Consequently, researchers are exploring noninvasive, cuffless methods that provide continuous and accurate blood pressure assessment. This article presents a comprehensive review of sensors and estimation models used in cuffless blood pressure monitors, with a focus on enhancing accuracy and minimizing calibration requirements. A literature search was conducted using Google Scholar and reputable journals, including IEEE, Frontiers, and MDPI, resulting in the selection of 35 relevant studies. The review examines innovative techniques based on electrical, mechanical, and optical sensors. Particular attention is given to photoplethysmography (PPG), electrocardiography (ECG), and bioimpedance (Bio-Z), which, when combined with advanced signal analysis and deep learning models, show promising results. PPG enables blood volume measurement at accessible sites like the fingertip or wrist, leveraging parameters such as pulse transit time. ECG, which directly reflects heart activity, is also widely used for blood pressure estimation. Recent advancements in machine learning have improved accuracy, with models such as HGCTNet (a hybrid CNN-Transformer architecture) achieving an error margin of 0.9 ± 6.5 mmHg for diastolic and 0.7 ± 8.3 mmHg for systolic blood pressures. Despite the potential, challenges remain, including the need for continuous calibration of PPG-based systems. Ongoing research aims to address these limitations by improving signal quality and developing robust algorithms. The demonstrated accuracy and reduced calibration requirements suggest that cuffless blood pressure monitoring technologies may soon become viable for widespread clinical and home use.

The area of 4D printing is a revolutionary one that emerged from the integration of additive manufacturing and polymer science. In this area, materials can change dynamically in response to external stimuli. Complex structures with developing, shape-morphing, and adaptive behaviors can be created by integrating innovative 3D printing technology with intelligent materials, which are naturally sensitive to environmental cues. Polymer chemistry determines these smart materials and discusses the design concepts that control their responsiveness. A particular focus is on incorporating stimuli-responsive polymers, like hydrogels and shape memory polymers, into the 4D printing process. These ensuing structures suggest programmable behavior in response to light, humidity, and temperature variations in the environment. 4D printing has been designated a revolutionary technology with significant implications for sectors looking for dynamic, responsive, and personalized solutions. The study of polymer-based innovative materials in 4D printing presents novel prospects for ingenuity, presenting extraordinary chances for creating and producing materials with dynamic properties. The future of advanced manufacturing is being shaped by this research, which advances our fundamental understanding of intelligent materials and opens up novel applications in various fields.

This study systematically evaluates the consistency and applicability limits of photoplethysmography (PPG)-derived pulse rate variability (PRV) versus electrocardiogram (ECG)-derived heart rate variability (HRV) in real-world settings. It integrates three methodological dimensions: 24-hour multi-context monitoring, dual-level consistency analysis (inter- and intra-individual), and controlled motion intensity via a 27-level acceleration gradient. Data from 14 healthy participants were collected using synchronized wrist-worn PPG, portable ECG, and triaxial accelerometry. Standardized preprocessing and motion artifact suppression based on acceleration thresholds enabled the extraction of time-domain, frequency-domain, and nonlinear HRV and PRV metrics. Consistency was assessed using Pearson correlation and root mean square error, with false discovery rate-corrected significance testing. Results show strong PPG-ECG agreement during sleep ( r > 0.9 for HR, MeanNN, Prc80NN) but marked degradation under high motion. Notably, Prc20NN demonstrated exceptional robustness across contexts, retaining significant correlation even during active phases. Stringent motion filtering substantially improved correlations. These findings delineate metric-specific validity boundaries for wearable PRV, distinguishing motion-induced errors from inherent physiological discrepancies, and offer evidence-based recommendations for deploying PRV in context-appropriate applications such as sleep monitoring, passive health tracking, and longitudinal stress assessment.

Ataxia is a progressive neurological disorder that impairs motor and functional ability due to cerebellar dysfunction. Conventional therapies, which include pharmacological interventions, offer limited benefits, creating a need for mechanism-based and objectively measurable alternatives. Current therapeutic strategies increasingly focus on neuromodulation techniques, physical training, hybrid protocols, and the use of smart wearable technology in some of these therapies. This review compares the clinical efficacy of physical therapy, neuromodulation techniques—deep brain stimulation, repetitive transcranial magnetic stimulation, and transcranial direct current stimulation (tDCS)—and their hybrid therapy modalities, aiming to assess how stimulation-induced neuroplasticity interacts with motor training to optimize rehabilitation techniques across hereditary and acquired ataxias. Gait indices such as the Scale for the Assessment and Rating of Ataxia, International Cooperative Ataxia Rating Scale, Berg Balance Scale, Timed Up and Go, stimulation settings, and intensity of the therapy were considered for evaluating the impact of the intervention. All interventions demonstrated short-term gains in coordination, gait, and balance. However, hybrid protocols, which integrate different physical rehabilitation techniques or a neuromodulation technique paired with physical therapy, showed stronger and more durable recovery. Mechanistically, neuromodulation models induce neuroplasticity in cerebellar-cortical pathways, while physical therapy stabilizes neuroplastic adaptations. Robot-assisted, remote tDCS interventions and wearable sensor-supported monitoring have increased ease of access and participant compliance. Limitations across studies included small cohorts, variability in stimulation parameters, and short follow-up durations. Collectively, hybrid and technology-integrated rehabilitation is a promising framework for reinforcing motor function and independence in ataxic patients. Future multicenter trials incorporating wearable gait biomarkers, neuroimaging, and personalized strategies are required to validate long-term efficiency and enable precision in therapies for ataxic patients.

Aphakia in the absence of adequate capsular or zonular support remains one of the most demanding challenges in anterior segment surgery. The condition may arise secondary to trauma, hereditary connective tissue disorders such as Marfan syndrome, complicated cataract surgery, or long-term pseudophakia with in-the-bag intraocular lens (IOL) dislocation. Our study describes outcomes of a modified two-point Canabrava scleral fixation technique, using 6-0 polypropylene sutures, combined with cerclage pupilloplasty for anterior segment reconstruction in eyes with zonular and iris damage. In our study, eight eyes have been treated with modified two-point scleral fixation between 2021 and 2025. The treatment involves external docking of the suture, elimination of intraocular docking combined with cerclage pupilloplasty, where it is indicated. Mean preoperative best-corrected visual acuity is 0.05 ± 0.04, improved to 0.80 ± 0.14 at 1 month. Mean intraocular pressure decreased from 21.0 ± 8.7 mmHg to 14.3 ± 1.2 mmHg. All IOLs remained well-centered, pupils were round and reactive, and no intraoperative/postoperative complications occurred. The main limitation of this case series is the retrospective design and limited sample size of eight cases using the modified technique. A larger, prospective comparative study with long-term follow-up is needed to confirm these preliminary results and to assess potential late flange degradation or suture-related changes beyond 2–3 years. This modification simplifies scleral fixation by externalizing the docking step, offering a safe, minimally invasive method in complex eyes with absent capsular support.

Innovations that help people with special needs are among the most important services scientific research can provide to serve societies. From this standpoint, the idea behind this work is to help blind and visually impaired people live their lives normally. For example, it is difficult for blind people to walk independently; they must rely on others in most of their daily activities. Walking on the streets is one of the most difficult problems that blind people face, as they cannot notice every obstacle on the road using a cane. From this perspective, the proposed model in this work served this sector of society and made it more effective. The model presented in our work includes designing a smart shoe based on Internet of Things technologies, a Mobile App, sensors, Raspberry Pi microcontrollers, a camera, and an integrated alarm device. The smart shoe offers a long-term solution for the visually impaired. Moreover, it will allow them to reach their destination without stress and independently. The model was tested under different conditions, and the results were satisfactory, as the accuracy reached 80%.