Search Results (873)

Traditional acupoint localization methods rely heavily on manual operation, resulting in high subjectivity and limited accuracy. To improve the precision and stability of acupoint detection, this study integrates machine vision technology with in situ projection to achieve automated recognition and real-time visualization of human acupoints. First, an automatic calibration method based on image processing is proposed for back acupoints. Spinal features are extracted from the blue channel, enhanced using adaptive histogram equalization, and processed through region of interest extraction, minimum-threshold binarization, and morphological operations. Key spinal curve points are then fitted using Bézier functions. Canny edge detection is used to extract the human silhouette, locate the acromion, and derive the pixel scale of the “cun” measurement, enabling coordinate computation for 141 back acupoints. In the deep learning component, an improved YOLOv8-Pose model is developed for acupoint localization. Unlike existing methods that use local attention or the original Object Keypoint Similarity (OKS) loss, we introduce two innovations: a non-local attention module for global dependency modeling, and a novel Efficient Object Keypoint Similarity (EOKS) loss function that incorporates geometric constraints—namely, width, height, and center distance—in addition to Euclidean distance. A non-local attention mechanism is incorporated into the backbone to enhance global feature extraction, and the EOKS loss function is designed to improve spatiogeometric regression accuracy. An inference mechanism is further introduced to derive the remaining acupoints from 49 detected keypoints; experiments demonstrate that the improved model achieves 95.0% detection accuracy, outperforming the baseline by 2.62%, with an inference time of 14.5 ms. Finally, an in situ projection platform is constructed, combining camera calibration, four-point proportional scaling, and an OpenCV 4.5.4-based interactive interface. The system supports real-time translation, rotation, and scaling, enabling accurate projection of detected acupoints onto the human body. Full article

Crafting STEM teaching into meaningful experiences can transform facts into knowledge. Immersive virtual reality (IVR) represents a significant pedagogical disruption, offering novel modalities of engagement with science content, extending beyond passive reception towards enhanced critical inquiry, reflective evaluation, and the cultivation of higher-order thinking skills. This study investigated how 20 Maltese students (mean age 12) adjusted their perceptions and acceptance of IVR when encountering it for the first time in formal STEM education. A quasi-experimental design was employed over six weeks, with data collected through pre- and post-intervention questionnaires. The analytical framework combined the Technological Frames of Reference (TFR) and Technology Acceptance Model (TAM) to capture perceptual snapshots and attitudinal shifts. While IVR initially stimulated enthusiasm, sustained exposure prompted critical reflections on its potential and limitations, particularly in relation to subject relevance, peer communication, and ease of use. Such deliberations are themselves suggestive indicators of reflective engagement. Rather than being demonstrated evidence of cognitive skill development, they are consistent with the early exercise of analytical and evaluative reasoning. These insights underscore the recursive dialog between technology-in-use and user contextualization, revealing how perceptions mature through experience. By examining how young learners engage with emergent technologies, this research highlights education’s role in cultivating adaptability, reflective judgment, and critical thinking capacities—central to innovative pedagogy and support for uncertain futures. Full article

Visual impairment affects approximately 2.2 billion people worldwide, yet existing assistive technologies remain fragmented and prohibitively expensive. This paper presents Munir, an integrated multimodal assistive system designed to enhance human–computer interaction through a combination of a mobile application and Bluetooth-enabled smart glasses. Munir leverages a hybrid machine learning architecture to provide inclusive, real-time support for daily living activities. The system integrates ten core capabilities—including face recognition, optical character recognition, and scene description—all accessible through a unified bilingual (Arabic/English) voice interface. By employing on-device processing for biometric tasks, Munir ensures user privacy and trust while maintaining high responsiveness. End-to-end system evaluation on the SCface dataset achieves a 96.69% recognition rate with 0% False Accept Rate. At an estimated first-year total cost of $806, Munir demonstrates a 4–5× cost advantage over commercial alternatives, providing a scalable and affordable multimodal solution for global digital inclusion. Full article

The 9th National Congress of the Italian Society for Virology (SIV-ISV), entitled “One Virology—One Health”, took place in Turin at the Centro Congressi Lingotto from 22 to 24 June 2025. The meeting highlighted recent multidisciplinary and translational developments in virology, with a strong focus on the integration of the One Health perspective. Major themes included viral emergence and surveillance, genomic sequencing and bioinformatics, virus–host interactions, viral immunology and vaccines, structural and physical virology, environmental and food virology, zoonoses and animal infections, diagnostics and antiviral therapy, virus-based biotechnology and plant virology. The Congress aimed to: (i) bring together clinicians, basic researchers, veterinarians, environmental microbiologists, bioinformaticians, public-health professionals and industry to share methodologies and best practices; (ii) provide an interactive scientific environment promoting discussion and collaboration between senior investigators and trainees through plenaries, joint society sessions, invited talks, oral communications selected from abstracts, poster sessions, and mentoring panels; and (iii) identify priorities and inspire new research directions at the interface of human, animal and environmental health. More than 400 participants from national and international institutions attended the meeting, featuring distinguished plenary speakers, joint sessions with global networks, and numerous presentations of original unpublished data. This report summarizes the meeting’s scientific highlights, cross-disciplinary discussions, and proposed actions to strengthen One Health surveillance, computational infrastructures, and translational applications of viral biology. Full article

The rate control algorithm is designed for natural video by default in video-coding standards. However, computer-generated screen content video (SCV) is very different from natural video captured by a camera, with many different statistical characteristics, such as sharp edges, thin lines, and flat area. This will lead to a difference in the focus of the human visual system (HVS) when viewing on-screen content video. Especially in various sensor data visualization applications such as intelligent display terminals, industrial monitoring and human–computer interaction interfaces, screen content video carries key information collected and reconstructed by image sensors, vision sensors and multimodal sensors. Its edge structures and local details directly affect the interpretation accuracy and application reliability of sensor information. Therefore, it is crucial to investigate perceptual rate control methods that integrate both video content characteristics and human visual perception properties, which possesses substantial theoretical and practical significance. In this paper, we propose a perceptual rate control algorithm for screen content video based on just-noticeable distortion (JND) which is established on the edge profile reconstruction with tolerable variations. First of all, target bit rate allocation for the frame level and CTU level is based on a perceptual weight which is calculated on the JND factor and reconstruction edge character. Secondly, under the constraint of the JND model, an intra rate-distortion (RD) model is established under the constraint of the JND model. The similarity between reference frames and reconstructed frames is taken as feedback in this model. Finally, the proposed rate control algorithm (JND–perceptual rate control (PRC)) is integrated to the existing rate control framework in High-Efficiency Video Coding–Screen Content Coding (HEVC-SCC) for improving the coding efficiency. The experimental results show that the proposed algorithm achieves better bit control precision than the platform, as well as improves the R-D performance of screen content video. In particular, compared with the HEVC-SCC reference software, the coding performance is improved by 3.09 dB on average, the bit rate is saved by 26.51% on average, and the average bit rate mismatch is within 1.159%. Full article

Patients with neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) frequently lose the ability to communicate through speech or writing. However, their cognitive and sensory functions are often relatively preserved. In Japan, the traditional method known as kuchimoji (mouth-based character communication) enables character-by-character communication using mouth shapes. This method relies heavily on caregiver skill and is challenging to implement consistently. This study introduces KUCHIMOJI, a Japanese text input system that uses mouth-shape recognition to support independent augmentative and alternative communication (AAC) without caregiver assistance. The system employs a lightweight convolutional neural network (MobileNetV2) to classify six mouth shapes. These shapes correspond to five vowels and a closed-lip state. To accommodate diverse user conditions, a multimodal input framework is designed. It supports three operation modes: facial-image-based signal input, button-based input, and key-based direct input. As an initial feasibility study, experiments with ten healthy participants were conducted to evaluate text entry performance in terms of text entry speed (TES) and miss entry rate (MER). Results indicate that the system achieves average input speeds of 3.86, 5.32, and 11.35 characters per minute (cpm) for the facial-image, button, and key-based modes, respectively. It maintains low error rates (2.96–5.05%). These findings suggest that the system offers a flexible trade-off between speed and accuracy depending on the input modality. The proposed approach provides a practical, low-cost, non-contact communication solution. This underscores its potential forpractical assistive communication applications. Full article

Environmental monitoring in hazardous indoor zones conventionally relies on fixed-sensor networks or manual inspections, both of which suffer from spatial blind spots and increased human exposure risks. This paper addresses the problem of transforming sparse, mobile sensor measurements into spatially resolved risk assessments in GPS-denied environments. We propose a Hazard Index (HI) framework that normalizes environmental parameters against established safety thresholds into a unified, graduated risk metric with $O\left(N\right)$ computational complexity, where N is the number of monitored parameters. The framework is designed for multi-parameter hazard assessment; the present work validates the computational pipeline, spatial mapping methodology, and classification logic through single-parameter CO₂ detection ($N=1$) deployed on a LiDAR-guided robotic platform integrating an MQ-135 gas sensor interfaced via a NodeMCU ESP8266 microcontroller. Experimental validation across a 144 sq ft indoor area achieved a trajectory-following RMSE of 0.54 ft relative to planned waypoints using Hector SLAM without odometry, detected CO₂ concentrations ranging from 0.02% to 0.25%, and identified a hazardous region encompassing eight measurement points ($\mathrm{HI}\ge 1.0$) using a three-tier classification scheme (Safe, Elevated, Hazardous) within 225 s of active mapping. The framework provides a lightweight computational footprint suitable for real-time evaluation on an NVIDIA Jetson Nano. The proposed approach establishes a cost-effective, reproducible methodology for autonomous indoor environmental monitoring, with the modular architecture designed for future expansion to multi-parameter sensing. Full article

Designing an optimal digital pathology workspace is essential to ensure diagnostic accuracy and safeguard the long-term well-being of pathologists. While digital pathology improves reproducibility, facilitates multidisciplinary collaboration, and supports data-driven precision medicine, its clinical effectiveness depends not only on computational performance but also on the physical and ergonomic environment in which pathologists operate. Inadequate workstation design may impair visual perception, increase cognitive and musculoskeletal strain, and potentially affect diagnostic consistency. Moreover, the progressive integration of artificial intelligence (AI) into routine diagnostics introduces additional requirements related to display performance, visualization interfaces, and human–machine interaction. Despite the rapid global adoption of digital pathology systems, standardized recommendations addressing ergonomic, environmental, and technological aspects of the digital workspace remain limited. In this work, we propose a clinically oriented framework for the design of digital pathology workspaces suitable for AI-assisted diagnostics. Key elements include the selection and calibration of medical-grade displays, ergonomic furniture and input devices, optimized ambient lighting conditions, and institutional quality assurance procedures. Emerging developments, such as intelligent ergonomic monitoring, advanced visualization interfaces, and adaptive AI-assisted workflows, may further support safe, sustainable, and high-performance digital diagnostic environments. Full article

Demand-responsive transit (DRT) is increasingly promoted as a means to enhance the resilience and inclusiveness of sustainable urban mobility. However, how users form early-stage adoption intentions toward such interface-mediated services remains insufficiently understood. While prior research has focused on conventional transit or mature mobility-on-demand platforms, the role of fine-grained human–computer interaction (HCI) characteristics in shaping initial adoption intentions toward DRT received limited empirical attention. This study proposes an integrated framework linking five HCI characteristics—interaction responsiveness, real-time interaction, controllability of interactivity, personalization of interactivity, and playfulness—to behavioral intention through the mediating mechanisms of perceived service quality and platform trust. The framework was tested by applying partial least-squares structural equation modeling to cross-sectional survey data (N = 147) collected from existing early users of an early-stage DRT pilot in Wuxi, China. Platform trust emerged as the strongest direct predictor of behavioral intention, while real-time interaction and interaction responsiveness contributed mainly through trust- and service-quality-based pathways. Controllability and personalization showed no statistically significant association with the mediators in this early-stage sample, and playfulness exhibited a significant but modest effect on platform trust. By integrating HCI design, service-quality perceptions, and platform trust into a single nomological framework, this study offers context-sensitive guidance for designing interface-mediated shared mobility services that may support more resilient and sustainable urban transport. Full article

The SARS-CoV-2 spike protein has been shown to activate Toll-like receptor 4 (TLR4), yet the precise molecular structures driving recognition and subsequent activation remain poorly defined. Here, we present in silico structural alignments and molecular docking simulations indicating potential spatial compatibility between the wild-type SARS-CoV-2 HR1HR2 fusion core and the human TLR4/MD-2 heterodimer. The computational models project candidate interfaces involving salt bridges, as well as polar and non-polar interactions, with both TLR4 and MD-2 dimerization partners, suggesting a theoretical topology compatible with the dimerization of two TLR4/MD-2 heterocomplexes. Notably, similar structural compatibility was modeled for related class I fusion proteins from other highly pathogenic viruses, including SARS-CoV, MERS-CoV, influenza viruses A, B, and C, respiratory syncytial virus (RSV), and partially Ebola virus. These findings offer an exploratory computational hypothesis regarding viral–host interactions with the host innate immune system, which can trigger immune recognition or detrimental hyperactivation. Full article

Soft robotics enables inherently safe, compliant interaction, yet integrating brain–computer interfaces (BCIs) remains hindered by a fundamental mismatch: BCIs typically output low-bandwidth, discrete commands, whereas soft robots possess high-dimensional, nonlinear dynamics. In this position paper, we argue that BCI–soft robot integration must move beyond direct decoder-to-actuator mapping. We propose a unified, application-oriented compatibility framework that structurally decouples hierarchical control and formally allocates authority between human neural input and local soft robotic autonomy. Crucially, we introduce verifiable, quantitative design principles that define integration as a matching problem across neural bandwidth, update frequency, latency tolerance, and control dimensionality. Through these testable hypotheses, we demonstrate that active, reactive, and passive BCIs serve distinct, complementary roles. We conclude that shared-control strategies—where the BCI provides high-level intent, target selection, or user-state feedback, while the soft robot manages low-level physical execution and interaction—offer the most practical pathway forward. We argue that future progress depends on the co-design of paradigm, decoding, control, and embodiment for neuro-adaptive and human-centred soft robotic systems. Full article

Plantar orthoses play an important role in podiatric care, as they help to redistribute plantar loads, improve foot function, and support the treatment of various conditions, including diabetic foot disease. In this context, additive manufacturing has substantially expanded the capacity to produce customized orthoses through digital geometry acquisition, computational design, and controlled fabrication. From a biomimetic and bionic perspective, 3D-printed plantar orthoses can be understood as engineered interfaces that reproduce, support, or modulate key biomechanical functions of the human foot, including load redistribution, shock attenuation, adaptive stiffness, and gait stabilization. Additive manufacturing enables these biological and biomechanical principles to be translated into patient-specific devices through controlled geometry, graded structures, and material selection. Moreover, from a sustainability perspective, recycled polylactic acid (rPLA) has emerged as a material of potential interest for this type of application, not only because of its compatibility with 3D-printing processes but also because it offers the possibility of reusing polymer waste and reducing the consumption of virgin raw materials in devices whose service life may be limited. This review examines the conditional viability of recycled PLA for 3D-printed plantar orthoses by integrating direct clinical evidence on orthotic function with indirect technical evidence from material-level and process-level studies. The reviewed literature indicates that recycled PLA may offer environmental and economic benefits; however, repeated thermomechanical reprocessing may alter viscosity, dimensional consistency, crystallinity, interlayer adhesion, and mechanical reliability. Recent orthosis-focused studies show that extrusion-based technologies can be applied to customized insoles, lattice or internally reinforced structures, multimaterial systems, and emerging smart concepts; however, most of these developments still rely on virgin or ad hoc-designed materials rather than recycled feedstocks. Overall, the available evidence suggests that recycled PLA should not yet be regarded as a direct substitute for virgin PLA in plantar orthoses. At present, the evidence supporting the use of recycled PLA in plantar orthoses is predominantly indirect and technical rather than directly clinical. Its use appears technically promising, but its viability remains conditional and depends on feedstock traceability, control of the manufacturing process, the suitability of material properties for device function, and validation of the orthosis under clinical conditions. Full article

Human activity recognition (HAR) using Wi-Fi channel state information (CSI) offers a privacy-preserving and contactless sensing modality suitable for smart homes, healthcare monitoring, and pervasive mobile Internet of Things (IoT) environments. However, existing CSI-based HAR approaches often suffer from computational inefficiency, high latency, and limited feasibility on resource-constrained embedded platforms. This work presents STAR (Sensing Technology for Activity Recognition), an edge AI-optimized framework that integrates lightweight temporal modeling, adaptive signal processing, and hardware-aware co-optimization to enable real-time, energy-efficient HAR on low-power embedded devices. STAR employs a streamlined three-layer Gated Recurrent Unit (GRU) architecture that reduces model parameters by 33% compared to conventional Long Short-Term Memory (LSTM) designs while maintaining strong temporal modeling capability. To enhance signal quality, STAR incorporates a multi-stage pre-processing pipeline consisting of median filtering, an eighth-order Butterworth low-pass filtering, and empirical mode decomposition (EMD) to denoise CSI amplitude measurements and extract stable spatial-temporal features. For on-device deployment, the system is implemented on a Rockchip RV1126 processor equipped with an embedded Neural Processing Unit (NPU) and interfaced with an ESP32-S3 CSI acquisition module. Experimental results demonstrate a mean recognition accuracy of 93.52% across seven activity classes and 99.11% for human-presence detection using a compact 97.6k-parameter model. INT8-quantized inference achieves a processing throughput of 33 MHz with only 8% CPU utilization, achieving a six-fold improvement in inference speed over CPU-based execution. With sub-second response latency and low power consumption, the system ensures real-time, privacy-preserving HAR, offering a practical, scalable solution for mobile and pervasive computing environments. Full article

Robotic assistance in minimally invasive surgery has significantly improved precision and dexterity; however, many supportive tasks, such as blood aspiration, still rely on manual operation. This work presents the design and implementation of a supervised autonomous robotic aspirator for detecting and removing bleeding in an in vitro experimental model. The proposed system integrates a perception module based on a convolutional neural network for real-time blood segmentation, a task planner for high-level action execution, and a control strategy based on artificial potential fields for autonomous navigation. Additionally, a mixed-reality human–robot interaction interface is incorporated to enable system supervision and seamless transition to teleoperation when required. The system was experimentally validated with a set of in vitro experiments under three representative bleeding scenarios, evaluating four suction strategies based on the computation method for the target selection. Results demonstrate high blood removal rates (above 80% in all cases) and high suction efficiency. The comparative analysis reveals that the performance of the suction strategies is scenario-dependent and highlights a trade-off between suction efficiency and removed area. These findings support the feasibility of autonomous robotic aspiration and provide insights into the design of adaptive strategies for surgical assistance, contributing toward increased task autonomy and reduced need for continuous manual suction control during minimally invasive procedures. Full article

Real-time physiological state awareness is central to next-generation wearable computing, yet most existing electrophysiological signal acquisition platforms remain limited to single-modality sensing, high component cost, or bulky form factors that hinder everyday deployment. Here, we present a compact, low-cost wearable platform for simultaneous electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) acquisition. The system integrates an analog front-end, a microcontroller, and a Bluetooth wireless link on a compact single-board platform (5.6 × 3.8 cm, approximately 12.8 g with the selected lithium-polymer battery installed), with an estimated bill-of-materials cost of 67.40 USD. Experimental validation across three healthy subjects, with the ECG channel additionally benchmarked against a commercial clinical-grade ambulatory ECG recorder, demonstrates that the platform captures ECG waveforms with recognizable P-QRS-T morphology under controlled recording conditions, supports reliable R-peak detection and heart rate estimation, records stable resting-state EEG spectral features, and distinguishes EMG activation from resting baseline in both time-domain amplitude and time-frequency structure. Leveraging the real-time wireless data link between the wearable hardware and a PC-hosted MATLAB environment, we further explore application-oriented signal processing scenarios. As an offline algorithm-pipeline compatibility demonstration, a CNN-based seizure detection pipeline is applied to the Bonn EEG benchmark for five-class epileptic state classification, achieving 86.60% mean classification accuracy. The proposed system offers a scalable and affordable foundation for wearable human-state-aware interaction, with potential applications in clinical monitoring, rehabilitation, and brain–computer interfaces. Full article