Search Results (844)

Torque-limiting mechanisms are safety-critical elements in mechanical, automotive, robotic, aerospace and medical systems, where controlled torque transmission is required to avoid overload failure. However, the influence of dent–slot geometry on torque stability, variability and tolerance sensitivity remains insufficiently quantified. This study examines the effects of dent height, dent angle and spring plate thickness on the torque response of a compact elastic, shape-dependent torque-limiting mechanism. An integrated methodology comprising conceptual design, mathematical modelling, theoretical analysis, finite element analysis, manufacturability assessment, material characterisation, dynamic testing and VIKOR-based decision-making was implemented. Five feasible spring-drive plate configurations were investigated using two dent heights, two dent angles and two spring plate thicknesses. Material and interface properties for the Ck67–SINT D39 tribological pair were determined through tensile, flexural and friction tests, while dynamic torque and output-force data were obtained using a dedicated test bench and statistically evaluated after Chauvenet-based removal of isolated peak values. The mathematical, theoretical, numerical and experimental results showed close agreement, with torque deviations below approximately 1.5% for the main comparison metrics. Increasing dent height from 1.40 to 1.80 mm reduced relative torque variability by 34.6%, whereas reducing the dent angle from 110° to 90° increased relative torque variability by 95.0%. Configuration A2 provided the best balance, confirming dent geometry as a controllable design variable. Full article

Background: Population ageing, increasing care demands, and rapid advances in artificial intelligence and robotics have intensified interest in care robots as potential tools to support independent living and complement human caregiving. However, the successful implementation of robotic technologies depends largely on public acceptance, which is influenced by functional, psychological, ethical, cultural, and generational factors. Objective: This study examined generational differences in the acceptance of care robots among Portuguese adults by integrating the Almere Model of technology acceptance with the Katz Index of Activities of Daily Living (ADL) and the Lawton–Brody Instrumental Activities of Daily Living (IADL) Scale. The research sought to determine whether acceptance varies according to generation and the type of caregiving activity performed by the robot. Methods: A cross-sectional quantitative study was conducted using an online questionnaire administered to a purposive sample of 235 adults residing primarily in the Lisbon Metropolitan Area, Portugal. The questionnaire combined constructs from the Almere Model with perceptions of robotic assistance for ADLs and IADLs. Principal Component Analysis, reliability analysis, descriptive statistics, and inferential analyses were performed to examine differences across generational groups. Results: Acceptance of care robots was strongly task-dependent. Participants expressed significantly greater acceptance of robots assisting with instrumental activities, including housekeeping, shopping, transportation, meal preparation, and medication management, than with intimate personal care activities such as bathing, dressing, toileting, feeding, and continence care. Contrary to common assumptions regarding digital natives, Generation Z reported higher levels of fear, discomfort, and perceived intimidation than Generation X and Baby Boomers. Older generations generally demonstrated more pragmatic acceptance of robotic assistance, particularly regarding future support needs associated with ageing. Across generations, respondents preferred robots with more human-like appearances; however, emotional trust remained substantially lower than perceived functional usefulness. Conclusions: The findings suggest that acceptance of care robots is conditional rather than universal and is shaped by the nature of the caregiving task, generational differences, and broader emotional and cultural perceptions of care. Integrating the Almere Model with established ADL and IADL frameworks provides a novel perspective by linking technology acceptance to specific functional domains of caregiving. The results support the view that care robots are more likely to be accepted as complementary tools that enhance human-centred care rather than as substitutes for professional or family caregivers. Given the purposive and geographically limited sample, the findings should be interpreted cautiously and not generalised to the wider Portuguese population. They nevertheless provide valuable implications for the design of socially assistive robots, healthcare practice, and public policy in ageing societies. Full article

Curves constructed directly on point clouds are a core primitive in reverse engineering, product design, and point-based CAD; many workflows additionally require the curve to lie in a plane—e.g., as a section profile, inspection path, or design reference. This paper presents an algorithmic framework for computing free and plane-constrained geodesic curves directly on oriented point clouds, without any intermediate surface or mesh reconstruction. A geodesic-curvature-minimizing solver that combines a Newton/conjugate-gradient flow with directed projection, elliptic Gabriel neighborhoods, and Taubin smoothing forms the backbone; the plane-constrained problem is then reduced to a one-parameter pencil of planes through the endpoint chord and solved per plane by alternating projection onto the cloud and the plane, with a projection-only pre-lift and a penalized length objective that rejects sections floating off the cloud; the returned section is the best found over a sampled pencil of candidate planes. The returned sections are attached to the cloud within a small fraction of the mean sampling distance. All algorithms are given in pseudocode with convergence criteria and complexity estimates. Two parallel realizations of the plane search are developed and measured: a multithreaded CPU backend (about 3× over the serial scan) and a WebGPU backend that evaluates the whole plane pencil in a single compute dispatch. Accuracy is validated against the analytic conic sections of a cone and against cylinder and sphere benchmarks whose optimal plane is known in closed form; robustness is assessed under noise, non-uniform sampling, missing regions, outliers, and perturbed normals, and against both a slab-projection baseline and the conventional reconstruct-then-slice route. Five applications—shoe-last reverse engineering with a C² surface reconstruction, anthropometric girth measurement, medical transverse sectioning, dimensional metrology on industrial mold scans, and cleaning-path planning for a robotic surface-treatment task—demonstrate the plane-constrained geodesic curves in practice. Full article

Motor imagery (MI) classification serves as the backbone to brain–computer interfaces (BCIs) by strengthening the communication bridge between the human brain and external peripheral devices. The past two decades have witnessed unprecedented success in MI-BCIs, with applications not only in medical fields but also in several other domains, such as gaming and robotic control. Initially, MI classification primarily relied on classical signal processing techniques that were heavily impacted by signal variations; however, recent trends in deep learning (DL), specifically in sequence-oriented, attention-based, hybrid, and generative architectures such as recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers have significantly improved the efficiency and robustness of MI classification. This study presents a comprehensive review of these sequence-oriented, attention-based, hybrid, and generative architectures including RNNs, VAEs, GANs, and transformers, comparing their robustness across various public MI datasets, highlighting their challenges, such as inter-subject variation, low signal-to-noise ratio (SNR), and the obstacles in real-time signal classification. We perform an in-depth analysis on the strengths and limitations of traditional models such as RNNs and LSTMs as well as emergent models such as VAEs and transformers, which have demonstrated superior performance in extracting the intricate patterns of the EEG data with low latency. Moreover, we critically examine the future potential of such models in overcoming current bottlenecks, such as weak generalization on unseen data and high computational load. This study aims to assist researchers in attaining significant insights into the state-of-the-art sequence, attention-based, hybrid, and generative models used in MI classification, thus offering a direction for future innovation. Full article

Flexible piezoresistive sensors are highly desirable for wearable health monitoring, yet balancing ultrahigh sensitivity and wide pressure detection range is a major bottleneck restricting their applications in electronic skin and soft robots. This work constructs a hierarchical piezoresistive sensor through a simple three-step fabrication: electrospinning PVDF/PAN fiber networks, polydopamine (PDA) surface modification, and in situ polypyrrole (PPy) polymerization for conductive sensing layers. As a dual-function interlayer, PDA forms hydrogen and covalent bonds with PPy to yield uniform, firm conductive coatings. The link between PPy morphology and sensing performance is clarified by regulating polymerization parameters. At a pyrrole concentration of 3 g/L, the optimized sensor achieves a high sensitivity of 220.88 kPa⁻¹ (0–10 kPa) and stable linear signals up to 1 MPa, with superior cycling durability over 5000 cycles and good biocompatibility. This scalable fabrication resolves the sensitivity–range tradeoff, promising wearable medical monitoring and human–machine interaction devices. Full article

Introduction: Crohn’s disease (CD) remains associated with long-term morbidity despite biologic therapies. Surgery, historically considered a last-resort option, is increasingly integrated into disease management earlier. This review examines the evolving role of surgery in the biologic era. Methods: A focused narrative review of randomized controlled trials, meta-analyses, observational studies, and international guidelines addressing contemporary surgical strategies in CD was performed. Results: The LIR!C trial demonstrated that early ileocecal resection provides durable remission and improves long-term outcomes compared with biologic therapy in selected patients. The PISA II trial supported an early combined surgical and medical approach for perianal fistulizing disease. Preoperative optimization, including nutritional support and adjustment of immunosuppressive therapy, has become a cornerstone of perioperative management. Laparoscopic surgery remains the preferred approach whenever feasible, while robotic surgery is emerging as a promising platform that is expected to play an increasingly important role in the surgical management of CD. Postoperative recurrence remains a major challenge, prompting the development of innovative surgical strategies. Although the Kono-S anastomosis initially showed promising reductions in recurrence, recent prospective studies failed to confirm superiority over conventional techniques. Similarly, extended mesenteric excision did not demonstrate improved outcomes despite increasing evidence implicating the mesentery in CD pathogenesis. Strictureplasty remains an effective option for selected fibrotic small-bowel strictures. Conclusions: Surgery remains central to multidisciplinary CD management and offers the potential to modify disease when performed early in selected patients. The impact of innovative surgical strategies on postoperative recurrence remains uncertain, and ongoing trials are expected to further help with surgical decision-making. Full article

Continual learning (CL) aims to develop intelligent systems capable of learning continuously from sequential data while retaining previously acquired knowledge. As AI systems are increasingly deployed in dynamic real-world environments, CL has become essential for enabling long-term adaptation without catastrophic forgetting. This review provides a structured overview of major CL paradigms, including task-incremental, domain-incremental, class-incremental, online, multimodal, and federated CL. We examine the theoretical foundations of CL, particularly the stability–plasticity dilemma, catastrophic forgetting, transfer dynamics, and representation learning. In addition, we analyze major methodological categories, including regularization-based, replay-based, architecture-based, optimization-based, representation-learning, and parameter-efficient approaches. Recent developments involving transformers, prompt learning, foundation models, and multimodal adaptation are also discussed as emerging directions in modern CL research. Furthermore, this review highlights important issues related to benchmark fragmentation, evaluation inconsistency, memory constraints, computational efficiency, scalability, and privacy-aware learning. We also summarize key application domains, including computer vision, natural language processing, robotics, healthcare, and medical imaging. Finally, we identify open research challenges and future directions toward scalable, reliable, and deployment-oriented lifelong learning systems capable of operating effectively in continuously evolving environments. Full article

Background: Autopsy pathology remains the reference standard for determining the cause of death, reconstructing disease and injury mechanisms, ensuring diagnostic quality, and supporting medical education and forensic investigations. However, declining autopsy rates, workforce shortages, biosafety concerns, increasing diagnostic complexity, and the rapid evolution of digital technologies have stimulated the development of complementary investigative approaches. This review critically examines whether artificial intelligence (AI) and emerging technologies are driving a genuine paradigm shift toward digitally integrated death investigation. Methods: A structured narrative review informed by a systematic literature search was conducted in PubMed/MEDLINE, Embase, Scopus, and Web of Science, covering publications from January 2000 through June 2026. Evidence addressing postmortem imaging, virtopsy, digital pathology, computational pathology, molecular autopsy, robotics, artificial intelligence, machine learning, and emerging omics technologies was critically appraised. Owing to the methodological heterogeneity of the available literature, findings were synthesized qualitatively according to technological maturity, forensic applicability, validation status, and implementation readiness. Results: The reviewed evidence demonstrates substantial progress in postmortem computed tomography, postmortem CT angiography, postmortem magnetic resonance imaging, whole-slide imaging, molecular autopsy, robotic-assisted postmortem procedures, three-dimensional reconstruction, and AI-assisted forensic analysis. These technologies enhance trauma evaluation, vascular imaging, ballistic reconstruction, digital documentation, remote consultation, diagnostic reproducibility, and multimodal integration of forensic evidence. Nevertheless, the level of evidence varies considerably across technological domains. Postmortem imaging represents the most mature and extensively validated technology, whereas most AI applications remain supported predominantly by retrospective proof-of-concept studies with limited multicenter external validation. Current systematic evidence further indicates that AI should presently be regarded as an assistive technology that augments expert forensic interpretation rather than replacing conventional autopsy or autonomous medicolegal decision-making. Conclusions: Contemporary autopsy pathology is evolving toward a hybrid model of digitally integrated death investigation in which conventional autopsy, imaging, digital pathology, molecular diagnostics, robotics, and AI function as complementary components of a unified forensic workflow. Current evidence supports a conceptual paradigm shift characterized by transformation of evidence acquisition, preservation, interpretation, and integration, while reaffirming that conventional autopsy remains the indispensable biological reference standard for the development, validation, and medicolegal interpretation of all emerging technologies. Future implementation should prioritize multicenter validation, standardized forensic datasets, explainable AI, digital chain-of-custody procedures, and robust regulatory governance to ensure safe and scientifically reliable integration into forensic practice. Full article

Haptic technology restores the sense of touch to robotic systems and has become increasingly important for safe and intuitive human–robot interaction in healthcare. Despite substantial advances over the past two decades, widespread clinical adoption remains limited, highlighting a persistent gap between laboratory research and real-world medical deployment. This review synthesizes research from robotics, human–computer interaction, neuroscience, and clinical medicine based on a systematic literature search conducted in IEEE Xplore, PubMed, and Scopus (2000–2025). The review adopts an information-centric perspective, focusing on the clinically relevant information conveyed through haptic feedback rather than force reproduction alone. The review examines tactile, kinesthetic, and hybrid feedback modalities; summarizes key principles of haptic rendering, stability, and control; and evaluates applications in surgical robotics, teleoperation, rehabilitation, prosthetics, and medical training. Evidence indicates that haptic feedback can improve performance, reduce excessive forces, and enhance situational awareness, although benefits remain task-dependent. Clinical translation continues to be constrained by sensing limitations, miniaturization challenges, stability requirements, human factors, and regulatory considerations. Current research is increasingly directed toward sensorless force estimation, artificial intelligence-assisted haptic rendering, wearable and soft haptic interfaces, and neurohaptic technologies, reflecting a shift toward task-oriented and information-centric feedback. Future progress will depend less on maximizing physical realism and more on delivering clinically meaningful information through stable, interpretable, and user-centered haptic systems. This review provides a roadmap for advancing clinically deployable haptic technologies in healthcare. Full article

Facial attribute segmentation is challenging because facial regions differ in size, shape, texture, and visibility, especially under non-frontal viewing conditions. This study evaluates the segmentation of four facial attribute classes: hair, eyebrows, mustache, and beard. A dataset of 5000 manually annotated FFHQ facial images was used to train and compare approaches within the same evaluation framework: YOLO segmentation, YOLO detection combined with SAM2 segmentation, and U-Net. The methods were evaluated on annotated test set, per facial attribute, and on an additional controlled phantom-based dataset acquired at five viewing angles. The hybrid YOLO detection and SAM2 segmentation pipeline achieved the best overall performance, with micro IoU of 0.820 and a micro Dice score of 0.893 on the annotated test set. Hair and beard are segmented more reliably than eyebrows and mustache, while segmentation accuracy decreased as the viewing angle increased. These results show that facial attribute segmentation performance depends on the selected method, target class, and acquisition viewpoint. The findings provide a basis for selecting and further improving segmentation methods for future registration and medical robotic applications. Full article

Background and Objectives: The recent adoption of RATS (robot-assisted thoracic surgery) alongside VATS (video-assisted thoracoscopic surgery) in minimally invasive thoracic surgery highlights the need for comparative evaluation of both techniques regarding postoperative pain and clinical outcomes. This study compared acute postoperative pain within the first 24 h, as well as postoperative complications, 30-day mortality, and length of hospital and ICU stay. Materials and Methods: A retrospective observational study was conducted including all patients scheduled for VATS or RATS at a tertiary hospital between November 2021 and December 2024. Demographic characteristics, surgical procedures, surgical approach, and pain-related outcomes at 24 h (Numeric Rating Scale [NRS], subjective assessment scale, and rescue analgesia) were obtained from the Acute Pain Unit database of the Department of Anesthesiology. Other variables were collected from the electronic medical record. Results: A total of 148 patients were analyzed, of whom 118 underwent VATS and 30 RATS. Surgical duration was significantly longer in the RATS group (130 vs. 218 min, p < 0.05). No significant differences were observed in NRS scores (2.57 ± 1.06 vs. 2.3 ± 0.79, p = 0.195) or subjective pain assessment (good: 78% vs. 83.3%, p = 0.472). RATS required less rescue analgesia in the unadjusted analysis (30.0% vs. 52.5% in VATS, p = 0.022); however, this association was no longer statistically significant after multivariable adjustment (VATS: OR 2.40, 95% CI 0.93–6.25; p = 0.071). There were no significant differences in postoperative complications (17.8% in VATS vs. 16.7% in RATS, p = 0.85), length of hospital stay (4.9 ± 6.2 days in VATS vs. 3.4 ± 3 days in RATS, p = 0.2), or 30-day mortality (0.8% in VATS vs. 0% in RATS, p = 1). ICU length of stay was longer in the RATS group (0.32 ± 0.78 days in VATS vs. 0.73 ± 1.23 days in RATS, p = 0.024). Conclusions: RATS did not demonstrate superiority over VATS in terms of postoperative pain, patient satisfaction, or clinical outcomes. Full article

Ultrasound is safe, portable, and relatively low cost, and robotic ultrasound research is expanding across many diagnostic applications. Within this context, abdominal aortic aneurysm (AAA) screening remains comparatively unexplored, with few systems reporting end-to-end autonomous scanning and clinician-validated evaluation in volunteers. We present a conditionally autonomous (Level-3) robotic ultrasound system in which the operator defines the region of interest and confirms the target force band, after which the robot performs surface-constrained abdominal sweeps under force control and automatically selects diagnostic frames and estimates aortic diameter without further manual interaction during scanning. The system combines RGB-depth-based patient-to-robot registration, hybrid position–force control with a low-cost force sensor, and a post-acquisition image-analysis pipeline comprising rule-based aorta localisation, a composite image quality assessment (IQA) metric, and a transfer-learned U-Net segmentation baseline. In a feasibility study on ten healthy volunteers spanning BMI 18.6–33 and diverse sex and skin-tone profiles, the robot maintained stable contact within the target force band in all sessions and produced aortic images rated diagnostically acceptable by clinicians in all participants. Automated diameter measurements showed a mean absolute difference of 1.45 mm relative to clinician reference values, with 9/10 cases within 3 mm and all within the 5 mm screening criterion. Volunteer questionnaires indicated high levels of comfort and trust in the system. These results demonstrate the feasibility of operator-supervised, force-aware robotic AAA scanning and highlight the potential of low-cost robotic ultrasound for wider automated vascular imaging. Full article

Robotic systems for pediatric rehabilitation must provide precise mechanical assistance while ensuring clinically appropriate risk control for vulnerable populations. In low- and middle-income countries (LMICs), academic medical robotics projects frequently fail to progress beyond intermediate Technology Readiness Levels (TRLs 3–5) due to limited translational planning. This study proposes and evaluates an integrated governance framework for academic pediatric rehabilitation robotics in LMIC settings, applied through the development of the AMCOR robotic orthosis for pediatric arthrogryposis multiplex congenita (AMC). The framework combines multiple national and international medical devices development standards and a dual regulatory pathway separating academic development from future translational stages. The framework is structured around four principles—auditability, TRL-proportional documentation, binding decision criteria, and regulatory separation—and is operationalized through a six-gate process. Across the first two gates (G0–G1), 38 traced requirements and 12 failure modes were documented. Five internal audits confirmed operational implementation of the quality management structure. The framework application also shaped core engineering decisions. The low amplitude and poor signal-to-noise ratio of sEMG signals observed in pediatric AMC patients rendered the original single-layer control strategy inadequate, prompting a framework-governed redesign toward a three-layer adaptive architecture based on signal quality thresholds and fallback safety logic. These findings demonstrate that a prospective, multi-standard governance model can improve early-stage academic medical robotics in resource-constrained settings. Generalizability beyond the single-center, two-gate application reported here requires further validation; however, the framework provides a replicable foundation for adoption in comparable LMIC contexts. Full article

Personalized oncology is transforming cancer care by tailoring therapeutic strategies to the molecular and clinical characteristics of individual patients. However, increasing treatment complexity, interpatient variability, and the growing use of advanced therapeutics challenge the limitations of standardized medicines. This review examines pharmaceutical compounding as a fundamental component enabling the delivery of individualized oncology treatments. A literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science, using a predefined search strategy detailed in the manuscript. This narrative review of the literature was conducted to evaluate the application of pharmaceutical compounding in modern oncology practice. The analysis includes immunotherapy, nanotechnology-based drug delivery systems, genomic-guided therapy, and combination treatment strategies. Emerging technologies, such as artificial intelligence, three-dimensional printing, and robotic compounding, were also assessed, alongside regulatory frameworks, safety challenges, and quality considerations. The main findings of this study show that compounded medications support individualized care through dose adjustment, modification of dosage forms, and exclusion of unsuitable excipients, particularly in pediatric oncology, rare cancers, and patients with specific needs. The magistral and officinal preparations help maintain continuity of care when commercial formulations are unavailable. In addition, technological advances are improving the precision, reproducibility, and safety of compounding processes, and pharmacists are centrally involved in the design, preparation, quality assurance, and regulatory oversight of these therapies. In conclusion, pharmaceutical compounding remains an essential component of personalized oncology, enabling patient-centered and adaptable treatment strategies. The expanding engagement of pharmacists, together with advances in technology and evolving regulatory frameworks, is essential to ensuring the safe and effective implementation of individualized therapies in oncology care. Full article

The rapid advancement of robot-assisted minimally invasive surgery (RAMIS) has created an increasing demand for integrated solutions that combine advanced robotic actuation, sensing, and intelligent control within unified training and operational frameworks. This paper presents a Digital Twin–based virtual reality (VR) interaction and control system developed for an innovative parallel surgical robot, designed to support both surgical training and real-time robot interaction. The proposed framework extends a conventional VR simulator into a bidirectional Digital Twin architecture, enabling real-time synchronization between a virtual environment and the physical robotic system. The system integrates the ATHENA parallel robot, characterized by a 4-degree-of-freedom architecture with a Remote Center of Motion (RCM) constraint, together with a flexible laparoscopic instrument providing enhanced dexterity. Interaction is achieved using VR controllers, allowing intuitive manipulation of the robotic system within an immersive environment. To enhance operational performance, an artificial intelligence module based on neural networks is integrated as an assistive component, providing real-time trajectory refinement and motion guidance. The trained model is deployed using an ONNX-compatible runtime, ensuring efficient inference and seamless integration within the control architecture. The proposed system is validated through experimental evaluation of user interaction and task execution performance, as well as through external motion assessment using an OptiTrack optical tracking system. The results demonstrate improvements in motion stability, execution efficiency, and user interaction quality, while maintaining a high level of control intuitiveness. The findings highlight the potential of Digital Twin–based VR systems as a unifying platform for surgical training, interaction, and intelligent assistance in next-generation medical robotic systems. Full article