Search Results (131)

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans. Full article

Passenger safety remains a primary goal in vehicle engineering, requiring the development of advanced passive safety systems to reduce injuries during collisions. Impact attenuators (particularly for race cars) are a crucial component for the safety of the driver. The impact of the impact attenuator (IA) is demonstrated by the behavior of a seat-belted dummy in a frontal collision with a rigid wall. The aim of this paper is to confirm the qualities of water as a damping agent in the manufacturing of the IA. To reach a conclusion, a theoretical model is used and experimental tests are performed. Once the loads operating on the dummy have been identified, it is confirmed that they fall within the range that the existing requirements recommend. The car is viewed as a structure with a seat-belt-fastened dummy and an impact attenuator. Research is being conducted on a new water-based impact attenuator technology. A frontal collision of the car–dummy assembly was taken into consideration when analyzing the dummy’s behavior in accordance with the criteria. A simulation program was used to calculate the accelerations at various points on the mannequin’s body as well as the force that manifested on the seat belts. So, the good qualities of IAs using water are revealed and support designers in their efforts to obtain better shock behavior. In the simulation, the variation of internal energy accumulated by the vehicle, displacements and velocities of various points on the chassis, as well as the accelerations of the vehicle and the occupant were determined. In the experiment, the vehicle velocities for both test cases were established and used in the simulation, and the accelerations of the vehicle and dummy were measured. The assessment was carried out by comparing experimental and simulation data, focusing on acceleration values recorded on both the dummy and the vehicle. Evaluation criteria such as HIC and ThAC were applied to determine the severity of the impact and the effectiveness of the proposed water-based attenuator. Full article

Background/Objectives: The potential advantages of robotic assistance in spinal procedures are a growing area of interest, and patient perception plays a key role in its broader acceptance. However, public perception of robotic surgery in spinal operations remains unexplored. This study aims to assess the general public’s perceptions, expectations, and concerns regarding robot-assisted spine surgery. Methods: In the fall of 2024, a questionnaire was distributed to attendees at a public open day at the Neurocenter of Southern Switzerland, where the Globus ExcelsiusGPS™ spine surgery robot was demonstrated live on a mannequin. The 15-item questionnaire assessed demographic data, prior knowledge of medical robots, mental representations of surgical robots, expectations, and emotions after witnessing the demonstration. Data were analyzed using descriptive statistics, chi-square, Wilcoxon, McNemar tests, and logistic regression analysis. Results: A total of 109 questionnaires were collected. Most participants were female (64.4%) and had no direct experience with spinal pathology (79.8%). While 87.2% were aware of robotic surgery in general, only 65.1% specifically knew about its use in spine surgery. After witnessing the live demonstration, 81.9% felt reassured by the robot′s presence in surgery, compared to 61.3% before the demonstration (p = 0.007). Preference for robot-assisted surgery increased from 50.5% to 64.5% (p < 0.001). Notably, individuals with back-related issues showed greater confidence in the robot’s capabilities (p = 0.032). Conclusions: The general public perceives robotic spine surgery positively, viewing it as faster, more precise, and capable of performing tasks not readily performed by humans. The study highlights the importance of live demonstrations in enhancing trust and acceptance of robotic systems. Its findings have economic implications, as patients may be more likely to choose hospitals offering robot-assisted spine surgery. However, it is essential to also acknowledge alternative methods, such as computer-assisted navigation, which has demonstrated efficacy in spine surgery. Full article

Hydrogel sensors show unique advantages in underwater detection, ocean monitoring, and human–computer interaction because of their excellent flexibility, biocompatibility, high sensitivity, and environmental adaptability. However, due to the water environment, hydrogels will dissolve to a certain extent, resulting in insufficient mechanical strength, poor long-term stability, and signal interference. In this paper, a double-network structure was constructed by polyvinyl alcohol (PVA) and poly([2-(methacryloyloxy) ethyl]7 dimethyl-(3-sulfopropyl) ammonium hydroxide) (PSBMA). The resultant PVA/PSBMA-PA hydrogel demonstrated notable swelling resistance, a property attributable to the incorporation of non-covalent interactions (electrostatic interactions and hydrogen bonding) through the addition of phytic acid (PA). The hydrogel exhibited high stretchability (maximum tensile strength up to 304 kPa), high conductivity (5.8 mS/cm), and anti-swelling (only 1.8% swelling occurred after 14 days of immersion in artificial seawater). Assembled as a sensor, it exhibited high strain sensitivity (0.77), a low detection limit (1%), and stable electrical properties after multiple tensile cycles. The utilization of PVA/PSBMA-PA hydrogel as a wearable sensor shows promise for detecting human joint movements, including those of the fingers, wrists, elbows, and knees. Due to the excellent resistance to swelling, the PVA/PSBMA-PA-based sensors are also suitable for underwater applications, enabling the detection of underwater mannequin motion. This study proposes an uncomplicated and pragmatic methodology for producing hydrogel sensors suitable for use within subaquatic environments, thereby concomitantly broadening the scope of applications for wearable electronic devices. Full article

With advancements in hardware, high-quality head-mounted display (HMD) devices are being developed by numerous companies, driving increased consumer interest in AR, VR, and MR applications. This proliferation of HMD devices opens up possibilities for a wide range of applications beyond entertainment. Most commercially available HMD devices are equipped with internal inward-facing cameras to record the periocular areas. Given the nature of these devices and captured data, many applications such as biometric authentication and gaze analysis become feasible. To effectively explore the potential of HMDs for these diverse use-cases and to enhance the corresponding techniques, it is essential to have an HMD dataset that captures realistic scenarios. In this work, we present a new dataset of periocular videos acquired using a virtual reality headset called VRBiom. The VRBiom, targeted at biometric applications, consists of 900 short videos acquired from 25 individuals recorded in the NIR spectrum. These 10 s long videos have been captured using the internal tracking cameras of Meta Quest Pro at 72 FPS. To encompass real-world variations, the dataset includes recordings under three gaze conditions: steady, moving, and partially closed eyes. We have also ensured an equal split of recordings without and with glasses to facilitate the analysis of eye-wear. These videos, characterized by non-frontal views of the eye and relatively low spatial resolutions ($400\times 400$), can be instrumental in advancing state-of-the-art research across various biometric applications. The VRBiom dataset can be utilized to evaluate, train, or adapt models for biometric use-cases such as iris and/or periocular recognition and associated sub-tasks such as detection and semantic segmentation. In addition to data from real individuals, we have included around 1100 presentation attacks constructed from 92 PA instruments. These PAIs fall into six categories constructed through combinations of print attacks (real and synthetic identities), fake 3D eyeballs, plastic eyes, and various types of masks and mannequins. These PA videos, combined with genuine (bona fide) data, can be utilized to address concerns related to spoofing, which is a significant threat if these devices are to be used for authentication. The VRBiom dataset is publicly available for research purposes related to biometric applications only. Full article

This paper explores the capability of large language models (LLMs) to accurately classify objects in challenging visual scenarios, focusing on two main tasks: differentiating real objects from artificial replicas and distinguishing human figures from human-like entities (e.g., mannequins, banners). We evaluate a diverse set of vision–language models (VLMs) ranging from large-scale architectures to parameter-efficient systems across multiple question prompts designed to probe object identification, authenticity verification, and multi-object reasoning. Our experiments reveal that while many models perform reasonably well in identifying single objects, their accuracy declines substantially under more complex conditions, such as multi-object scenes or tasks requiring fine-grained judgments of authenticity. Even top-tier models exhibit noticeable performance drops from around 100% to below 15% accuracy when forced to discern real from fake items among multiple candidates, and from 100% to 83.33% accuracy on providing positional details for human-like figures. We further discuss how these performance limitations indicate gaps in current LLM-based vision systems to highlight the need for more robust spatial reasoning and attribute analysis. Our findings underscore the significance of broadening these models’ multimodal understanding and refining prompts, with an eye toward improving real-world applications—from automated quality control to surveillance—where nuanced visual classification is crucial. By comparing a variety of architectures under consistent evaluation settings, this study offers insights into the barriers LLMs face when confronted with increasingly complex visual information. Full article

Compression stockings have long been manufactured in a single color without patterns, but enhancing their aesthetic appeal through knitted designs can improve user compliance. This study explores the potential of punch lace knitted structures to create patterns in compression textiles by seamless knitting technology while maintaining sufficient pressure. The effects of yarn material, number of yarns used, and knitted patterns on pressure and thermal comfort will be studied. The fabric pressure was evaluated using pressure sensors with a leg mannequin, while the thermal properties were measured according to the textile standard. This study found that the pressure and thermal conductivity of fabric are significantly influenced by the number of yarn and yarn materials, but not the knitted pattern. Cupro/cotton/polyurethane yarn (A) exhibits the strongest positive impact on pressure, increasing by 2.03 mmHg with the addition of one end of yarn A while polyamide/lycra yarn (C) exhibits a higher thermal conductivity than yarn A. For air permeability, the number of yarn and knitted patterns significantly affects the ventilation resistance. Pattern B with an additional needle in a float stitch shows 0.023 kPa·s/m lower resistance than pattern A. The findings from this study can be widely used in health, medical, and sports applications. Full article

Background: Despite the development of manual and passive mechanisms for adjusting prosthesis length, their efficiency remains limited, and users continue to face challenges related to leg length discrepancy (LLD). This dysfunction is an unresolved issue in prosthetics, and the lack of a prosthesis capable of automatically correcting LLD highlights a critical gap in the field. Methods: This study introduces a transtibial self-adaptive prosthesis capable of autonomously compensating for LLD. The prosthesis’s performance was evaluated using the ARAMIS 3D Digital Image Correlation (DIC) system, which monitored the displacement of markers strategically placed on a test mannequin in a strategic manner. Simulated LLDs were generated by inserting standardized plates beneath the mannequin’s intact limb. Results: The proposed prosthesis demonstrated efficient compensation of simulated LLD, with a mean relative error of 8.8% for a 1 mm simulation and 7.2% for a 10 mm simulation. The DIC system highlighted the rapidity and precision of the prosthesis mechanism in adjusting its length to eliminate LLD. Conclusions: The proposed transtibial self-adaptive prosthesis represents a significant advancement in the automatic correction of LLD, enhancing gait symmetry and user stability. The results suggest substantial potential for clinical applications, with promising implications for users of unilateral prostheses. Full article

Imaging sonar is a crucial tool for underwater visual perception. Compared to 2D sonar images, 3D sonar images offer superior spatial positioning capabilities, although the data acquisition cost is higher and lacks open source references for data annotation, target detection, and semantic segmentation. This paper utilizes 3D imaging sonar to collect underwater data from three types of targets with 1534 effective frames, including a tire, mannequin, and table, in Liquan Lake, Shanxi Province, China. Based on these data, this study focuses on three innovative aspects as follows: rapid underwater data annotation, loss function optimization, and unsupervised moving target extraction in water. For rapid data annotation, a batch annotation method combining human expertise and multi-frame superposition is proposed. This method automatically generates single-frame target detection boxes based on multi-frame joint segmentation, offering advantages in speed, cost, and accuracy. For loss function optimization, a density-based loss function is introduced to address the issue of overfitting in dense regions due to the uneven distribution of point cloud data. By assigning different weights to data points in different density regions, the model pays more attention to accurate predictions in a sparse area, resulting in a 6.939 improvement in mIOU for semantic segmentation tasks, while lakebed mIOU achieved a high score of 99.28. For unsupervised moving target extraction, a multi-frame joint unsupervised moving target association extraction method called the Double DBSCAN, D-DBSCAN, is proposed. This method simulates human visual sensitivity to moving targets in water and uses a joint D-DBSCAN spatial clustering approach with single-frame and inter-frame superposition, achieving an improvement of 21.3 points in mAP. Finally, the paper summarizes the three proposed innovations and provides directions for further research. Full article

Bracing is a widely used conservative treatment for adolescent idiopathic scoliosis (AIS) patients, yet there is no consensus on the optimal amount of force applied. Although a number of different sensors have been developed to continuously monitor the applied pressure and force, they have several limitations, including inadequate overall force distribution and displacement. They also cause discomfort with limited wearability. In this study, body pressure mapping knitwear (BPMK) integrated with fourteen silicone-embedded fiber Bragg grating (FBG) sensors is developed to monitor immediate and overall changes in force during the bracing treatment. A wear trial of the BPMK is conducted by using a validated soft AIS mannequin, and prediction equations have been formulated for the FBG sensors at individual locations. The findings indicate that the measured forces are in good agreement with those obtained from clinical studies, with peak forces around the padding regions reaching approximately 2N. This was further validated by using finite element (FE) models. When comparing X-ray images, the estimated differences in Cobb angles were found to be 0.6° for the thoracic region and 2.1° for the lumbar region. This model is expected to provide valuable insights into optimal force application, thus minimizing the risk of injury and enhancing bracing compliance and efficacy. Ultimately, this innovative approach provides clinicians with data-driven insights for safer and more effective bracing applications, thus improving the quality of life of AIS patients. Full article

Heat stress on workers wearing PPE (Personal protective equipment) in hot outdoor environments is of rising concern, especially in cases when rest breaks and clothing changes are impractical. Mist fan evaporative cooling could provide low-energy continuous cooling, even during work activity. The cooling effect of a misting fan was compared to that of a fan alone, as well as natural convection. A thermal mannequin with heat flux sensors at eight body locations was exposed to an outdoor misting fan while being clothed in typical work clothes and PPE. Work clothes were dry or saturated with water to simulate sweat. The distance from the misting fan ranged from 4 m (wetting common) to 7 m (wetting unlikely). On average, the misting fan had a cooling effect of 0.31 met (18.3 W/m²) higher than natural convection when PPE is worn with wet work clothes, and 0.35 met (20.3 W/m²) higher than when PPE is worn with dry work clothes. This equates to reducing the thermal metabolic load from light industrial work to walking about in office work, or from standing to reclining. Under the ISO 7243 international standard for workers in hot environments, this would increase the acceptable WBGT (wet bulb globe temperature) by over 0.6 °C. Full article

Despite the increasing incidence of myocardial infarction among middle-aged adults, studies analyzing their recognition of early myocardial infarction symptoms and cardiopulmonary resuscitation training experiences are lacking. This study aimed to utilize data from the 2022 Korea Community Health Survey to assess the level of recognition of early myocardial infarction symptoms among middle-aged adults and identify factors affecting cardiopulmonary resuscitation performance based on training experiences. Secondary data analysis was conducted to assess 99,945 adults aged 40–64 years on their recognition of early myocardial infarction symptoms and the influence of cardiopulmonary resuscitation training experience on their performance ability. Data analysis was performed using multinomial logistic regression, followed by assessing the area under the curve and visualizing the receiver operating characteristic curve to evaluate the model’s performance. The recognition of early myocardial infarction symptoms improved; the cardiopulmonary resuscitation performance ability increased by 22%. The absence of training with mannequins and automated external defibrillators resulted in a 79% and 77% decrease in cardiopulmonary resuscitation performance ability, respectively. Enhancing the recognition of early myocardial infarction symptoms and providing hands-on cardiopulmonary resuscitation training is vital for improving cardiopulmonary resuscitation performance in middle-aged adults. Effective cardiopulmonary resuscitation training programs can facilitate the rapid identification of patients who have had a cardiac arrest, bolster emergency response capabilities, and enhance the overall social safety net. Full article

Aircraft cabins have high occupant densities and may introduce the risk of COVID-19 contamination. In this study, a segment of a Boeing 767 aircraft cabin with a mixing type of air distribution system was investigated for COVID-19 deposition. A section of a Boeing 737-300 cabin, featuring four rows with 28 box-shaped mannequins, was used for simulation. Conditioned air entered through ceiling inlets and exited near the floor, simulating a mixed air distribution system. Cough droplets were modeled using the Discrete Phase Model from two locations: the centre seat in the second row and the window seat in the fourth row. These droplets had a mean diameter of 90 µm, an exhalation velocity of 11.5 m/s and a flow rate of 8.5 L/s. A high-quality polyhedral mesh of about 7.5 million elements was created, with a skewness of 0.65 and an orthogonality of 0.3. The SIMPLE algorithm and a second-order upwind finite volume method were used to model airflow and droplet dynamics. It was found that the ceiling accounted for the maximum concentration followed by the seats. The concentration of deposits was almost 50% more when the source was at window as compared to the centre seat. The Covid particles resided for longer duration when the source was at the centre of the cabin than when it was located near the widow. Full article

Objective: To develop and validate a device that measures the pressure exerted by forceps on the fetal head for clinical use. Background: The lack of clinical tools to quantify forceps pressure on the fetal head may impact maternal and neonatal outcomes. Existing studies have not measured the direct contact pressure between forceps blades and the fetal head, highlighting the need for innovation. Methods: We integrated fluid pressure transducers into obstetric forceps using fluid-filled tubing encased in flexible silicone socks attached to the blades. Tubing materials—polyvinyl chloride (PVC) and polyurethane (PU)—and fluids (air and water) were tested with both biocompatible and non-biocompatible silicone socks. An onboard electronic board collected pressure data and transmitted them via Bluetooth for real-time analysis. The system was evaluated on a custom-built bench simulating forceps application. Results: Air-filled tubing exhibited significant drift and low accuracy due to air compressibility. Water-filled PU tubing reduced drift but was still suboptimal. Water-filled PVC tubing with both types of silicone socks provided the best results, showing minimal drift and a strong correlation between measured pressures and applied forces. Conclusions: The developed device represents a significant advancement, as no existing system measures the pressure exerted by forceps blades on the fetal head. By effectively measuring pressure across the entire contact surface in real time, it offers applications in both training and clinical practice. The device allows for objective feedback, potentially improving the safety and efficacy of forceps deliveries. Future work includes comprehensive mannequin tests and eventual in vivo studies to validate its effectiveness in realistic settings, aiming to enhance obstetric training and reduce maternal and neonatal complications. Full article

A head-related transfer function (HRTF) is a mathematical model that describes the acoustic path between a sound source and a listener’s ear. Using binaural synthesis techniques, HRTFs play a crucial role in creating immersive audio experiences through headphones or loudspeakers, using binaural synthesis techniques. HRTF measurements can be conducted either with standardised mannequins or with in-ear microphones on real subjects. However, various challenges arise in, for example, individual differences in head shape, pinnae geometry, and torso dimensions, as well as in the extensive number of measurements required for optimal audio immersion. To address these issues, numerous methods have been developed to generate new HRTFs from existing data or through computer simulations. This review paper provides an overview of the current approaches and technologies for generating, adapting, and optimising HRTFs, with a focus on physical modelling, anthropometric techniques, machine learning methods, interpolation strategies, and their practical applications. Full article