Search Results (628)

Mitral valve repair has evolved significantly with the advent of advanced surgical and transcatheter techniques. Innovations such as 3D visualization, robotic surgery, and transcatheter edge-to-edge repair have improved procedural precision and expanded treatment options for high-risk patients. Emerging technologies, including transcatheter mitral valve repair, annuloplasty, and chordal systems, offer tailored solutions for complex mitral pathology. Personalized treatment strategies, guided by multimodality imaging and artificially intelligence-driven planning, are reshaping clinical decision-making. Ongoing trials and next-generation devices are poised to enhance long-term outcomes, marking a shift toward minimally invasive, precision-guided mitral valve therapy. This review aims to provide a comprehensive overview of recent technological advances, clinical applications, and future directions in mitral valve repair across surgical and interventional domains. Full article

Background/Objectives: The aging population faces a multitude of health challenges, particularly when it comes to maintaining proper nutrition. Age-related physiological changes, such as decreased metabolism, diminished taste perception, and difficulty in chewing, can lead to insufficient nutrient intake, ultimately resulting in malnutrition. It is crucial to address these issues to promote not only physical health but also overall well-being. In this modern era, artificial intelligence (AI) technologies, including robots and machine learning algorithms, are being increasingly harnessed to encourage healthy eating habits among older adults. This is critical to support healthy aging and mitigate diet-related chronic diseases. However, little or no synthesis has established their effectiveness in delivering personalized, scalable, and adaptive interventions for older adults. This systematic review considers the state-of-the-art application of AI-based interventions aimed at improving dietary behaviors and nutritional outcomes in older adults. Methods: Following the PRISMA 2020 guidelines and a registered PROSPERO protocol (ID: CRD420241045268), we systematically analyzed 30 studies we collected from five databases, published between 2015 and 2025 based on different AI techniques, including machine learning, natural language processing, and recommender systems. We synthesized data collected from these studies to examine the intervention types, outcomes, and methodological approaches. Results: Findings from our review highlight the potential of AI-based interventions to promote engagement among older adults and improve adherence to healthy eating guidelines. Additionally, we found some challenges related to ethical concerns such as privacy and transparency, and limited evidence of their long-term effectiveness. Conclusions: AI-based interventions offer significant promise in promoting healthy eating among older adults through personalized, adaptive, and scalable interventions. Yet, current evidence is constrained by some methodological limitations and ethical concerns, which calls for future research to design inclusive, evidence-based AI interventions that address the unique physiological, psychological, and social needs of older adults. Full article

Background/Objectives: Traumatic brain injury (TBI) is a major global cause of death and disability, with long-term cognitive, behavioral, and functional consequences. Despite its high burden, management is complicated by heterogeneous presentations and limited evidence. This review summarizes recent advances in monitoring, therapeutic strategies, neuroprotection, and rehabilitation, while highlighting future directions toward individualized and globalized care. Methods: This paper is a narrative review of clinical trials, systematic reviews, and observational studies, focusing on invasive and non-invasive monitoring, pharmacologic and non-pharmacologic interventions, neuroprotective agents, stem cell therapy, and advanced rehabilitation modalities. Results/Findings: Our review focuses on emerging monitoring techniques, including brain tissue oxygenation, cerebral microdialysis, and multimodal strategies, that provide detailed insights but lack standardized application. Interventions such as anti-inflammatory agents, hypothermia, hyperosmolar therapies, and metabolic suppression show mixed efficacy, with few therapies supported by high-level evidence. Novel agents like erythropoietin and progesterone demonstrate neuroprotective potential in preclinical models but remain inconclusive in clinical trials. Stem cell therapies and extracellular vesicle approaches are promising in early studies. Rehabilitation is expanding with virtual reality, robotics, and neurostimulation to promote neuroplasticity. Personalized medicine approaches incorporating biomarkers and machine learning may refine prognostication and guide therapy. Global inequities persist, particularly in low-resource settings. Conclusions: TBI care is shifting toward individualized, multimodal, and technology-driven strategies. While emerging therapies show promise, high-quality randomized trials and global implementation strategies are needed to improve outcomes and reduce disparities. Full article

The emergence of Industry 5.0 brings a paradigm shift towards collaborative environments where humans and intelligent robots work side-by-side, enabling personalized, flexible, and resilient manufacturing. However, integrating humans and robots introduces new operational and safety risks that require proactive and adaptive control strategies. This study proposes an intelligent multi-criteria decision-making framework for smart risk assessment and the selection of optimal adaptive control strategies in human–robot collaborative manufacturing settings. The proposed framework integrates advanced risk analytics, real-time data processing, and expert knowledge to evaluate alternative control strategies, such as real-time wearable sensor integration, vision-based dynamic safety zones, AI-driven behavior prediction models, haptic feedback, and self-learning adaptive robot algorithms. A cross-disciplinary panel of ten experts structures six main and eighteen sub-criteria spanning safety, adaptability, ergonomics, reliability, performance, and cost, with response time and implementation/maintenance costs modeled as cost types. Safety receives the most significant weight; the most influential sub-criteria are collision avoidance efficiency, return on investment (ROI), and emergency response capability. The framework preserves linguistic semantics from elicitation to aggregation and provides a transparent, uncertainty-aware tool for selecting and phasing adaptive control strategies in Industry 5.0 collaborative cells. Full article

Background: Mild cognitive impairment (MCI) affects up to 40% of patients with Parkinson’s disease (PD), yet conventional rehabilitation often lacks engagement. The OPERA project developed a novel Bio-cooperative Robotic Platform (PRoBio), integrating a service robot and a virtual reality-based rehabilitation for personalized cognitive training. This work presents two preliminary user-centred studies aimed to assess PRoBio usability and acceptability. Methods: to gather qualitative feedback on robotic and virtual reality technologies, through ad hoc questionnaires, developed according to participatory design principles and user-centered evaluation literature, Study 1 (Focus group) involved 23 participants: 10 PD patients (F = 6; mean age = 68.9 ± 8.2 years), 5 caregivers (F = 3; mean age = 49.0 ± 15.5), 8 healthcare professionals (F = 6; mean age = 40.0 ± 12.0). Study 2 (Living Lab) tested the final version of PRoBio platform with 6 healthy volunteers (F = 3; mean age = 50.3 ± 11.0) and 8 rehabilitation professionals (F = 3; mean age = 32.8 ± 9.9), assessing usability and acceptability through validated questionnaires. Results: The focus group revealed common priorities across the three groups, including ease of use, emotional engagement, and personalization of exercises. Living Lab unveiled PRoBio as user-friendly, with high usability, hedonic quality, technology acceptance and low workload. No significant differences were found between groups, except for minor concerns on system responsiveness. Discussion: These preliminary findings support the feasibility, usability, and emotional appeal of PRoBio as a cognitive rehabilitation tool. The positive convergence among the groups suggests its potential for clinical integration. Conclusions: These preliminary results support the feasibility and user-centred design of the PRoBio platform for cognitive rehabilitation in PD. The upcoming usability evaluation in a pilot study with patients will provide critical insights into its suitability for clinical implementation and guide further development. Full article

The field of IoT has a growing interest in robotics, and one of the big fields of this interest is human–robot collaboration. A sub-category of the human–robot collaboration is how a robot can follow a human. There are a few methods of following a person; one would be using image recognition, and another way could be using a rope attached to that person. Image recognition is compared to following a person with a rope to find out the benefits and weaknesses of both methods, comparing smoothness, responsiveness, and accuracy. With the comparison of these two systems, the findings presented in this study indicate that the overall responsiveness of the spool-based system is higher than the camera-based system, the directional responsiveness of the camera-based system is higher than the spool-based system, and the distance estimation of the camera-based system is noisier and less reliable than the spool-based system. Full article

The hospitality sector is undergoing a rapid digital change due to smart technology and artificial intelligence. This presents both possibilities and problems for the development of sustainable innovation. Yet, little is known about how leaders’ technological competencies affect employees’ capacity to engage in environmentally responsible innovation. This study addresses this gap by examining how leaders’ competencies in smart technology, artificial intelligence, robotics, and algorithms (STARA) shape employees’ green innovative behavior in hotels. Anchored in person–job fit theory and cognitive appraisal theory, we propose that when employees perceive a strong alignment between their skills and the technological demands introduced by STARA, they are more likely to appraise such technologies as opportunities (challenge appraisals) rather than threats (hindrance appraisals). These appraisals, in turn, mediate the link between leadership and green innovation. Convenience sampling was used to gather data from staff members at five-star, ecologically certified hotels in Sharm El-Sheikh, Egypt. According to structural equation modeling using SmartPLS, employees’ green innovation behaviors are improved by leaders’ STARA abilities. Crucially, staff members who viewed STARA technologies as challenges (i.e., chances for learning and development) converted leadership skills into more robust green innovation results. Conversely, employees who perceived these technologies as obstacles, such as burdens or threats, diminished this beneficial effect and decreased their desire to participate in green innovation. These findings highlight that the way employees cognitively evaluate technological change determines whether leadership efforts foster or obstruct sustainable innovation in hotels. Full article

Hand rehabilitation exoskeletons play a critical role in restoring motor function in patients with stroke or hand injuries. However, most existing designs rely on fixed-axis assumptions, neglecting the rolling–sliding coupling of finger joints that causes instantaneous center of rotation (ICOR) drift, leading to kinematic misalignment and localized pressure concentrations. This study proposes the Instant Radius Method (IRM) based on medical imaging to continuously model ICOR trajectories of the MCP, PIP, and DIP joints, followed by the construction of an equivalent ICOR through curve fitting. Crossing-type biomimetic kinematic pairs were designed according to the equivalent ICOR and integrated into a three-loop ten-linkage exoskeleton capable of dual DOFs per finger (flexion–extension and abduction–adduction, 10 DOFs in total). Kinematic validation was performed using IMU sensors (Delsys) to capture joint angles, and interface pressure distribution at MCP and PIP was measured using thin-film pressure sensors. Experimental results demonstrated that with biomimetic kinematic pairs, the exoskeleton’s fingertip trajectories matched physiological trajectories more closely, with significantly reduced RMSE. Pressure measurements showed a reduction of approximately 15–25% in mean pressure and 20–30% in peak pressure at MCP and PIP, with more uniform distributions. The integrated framework of IRM-based modeling–equivalent ICOR–biomimetic kinematic pairs–multi-DOF exoskeleton design effectively enhanced kinematic alignment and human–machine compatibility. This work highlights the importance and feasibility of ICOR alignment in rehabilitation robotics and provides a promising pathway toward personalized rehabilitation and clinical translation. Full article

Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot workload. Existing multi-objective evolutionary algorithms (MOEAs) often struggle with slow convergence and insufficient diversity when tackling the combinatorial complexity of large-scale MRTA instances. This paper introduces the Collaborative Swarm-Differential Evolution (CSDE) algorithm, a novel MOEA designed to overcome these limitations. CSDE’s core innovation lies in its deep, operator-level fusion of Differential Evolution’s (DE) robust global exploration capabilities with Particle Swarm Optimization’s (PSO) swift local exploitation prowess. This is achieved through a unique fused velocity update mechanism, enabling particles to dynamically benefit from their personal experience, collective swarm intelligence, and population diversity-driven knowledge transfer. Comprehensive experiments on various MRTA scenarios demonstrate that CSDE consistently achieves superior performance in terms of convergence, solution diversity, and Pareto front quality, significantly outperforming standard multi-objective algorithms like Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Differential Evolution (MODE), and Multi-Objective Genetic Algorithm (MOGA). This study highlights CSDE’s substantial contribution to the MRTA field and its potential for more effective and efficient multi-robot system deployment. Full article

Artificial intelligence (AI) enhances anesthesiology by introducing adaptive systems that improve clinical precision, safety, and responsiveness. This review examines the integration of AI in anesthetic practice, with a focus on closed-loop systems that exemplify autonomous control. These platforms integrate continuous physiologic inputs, such as BIS, EEG, heart rate, and blood pressure, to titrate anesthetic agents in real time, providing more consistent and responsive management than manual methods. Predictive algorithms reduce intraoperative hypotension by up to 40%, and systems such as McSleepy demonstrate greater accuracy in maintaining anesthetic depth and shortening recovery times. In critical care, AI supports sedation management, reduces clinician cognitive load, and standardizes care delivery during high-acuity procedures. The review also addresses the ethical, legal, and logistical challenges to widespread adoption of AI. Key concerns include algorithmic bias, explainability, and accountability for machine-generated decisions and disparities in access due to infrastructure demands. Regulatory frameworks, such as HIPAA and GDPR, are discussed in the context of securing patient data and ensuring its ethical deployment. Additionally, AI may play a transformative role in global health through remote anesthesia delivery and telemonitoring, helping address anesthesiologist shortages in resource-limited settings. Ultimately, AI-guided closed-loop systems do not replace clinicians; instead, they extend their capacity to deliver safe, responsive, and personalized anesthesia. These technologies signal a shift toward robotic anesthesia, where machine autonomy complements human oversight. Continued interdisciplinary development and rigorous clinical validation will determine how AI integrates into both operating rooms and intensive care units. Full article

This study compares immersive VR-based control systems with conventional keyboard-based control to examine the efficacy of VR interfaces for controlling robotic arms in Internet of Things (IoT) education. A 5-DOF robotic arm with MG996R servomotors and controlled by an Arduino microcontroller and Raspberry Pi wireless communication was operated by 31 third-year engineering students in hands-on experiments using both control modalities. To determine student preferences across in-person, online, and hybrid learning contexts, the study applied a mixed-methods approach that combined qualitative evaluation using open-ended questionnaires and quantitative analysis through Likert-scale surveys. First, it should be mentioned that most of the reported papers either use a robotic arm or a VR system in education. However, we are among the first to report a combination of the two. Secondly, in most cases, there are either technical papers or educational quantitative/qualitative research papers on existing technologies reported in the literature. We combine an innovative education context (robotic arm and VR), completed with a quantitative and qualitative study, making it a complete experiment. Lastly, combining qualitative with quantitative research that complement each other is an innovative aspect in itself in this field. Full article

The integration of AI-powered plush robots in educational and therapeutic settings for children with Autism Spectrum Disorders (ASD) necessitates a robust interdisciplinary methodology to evaluate usability, psychological impact, and therapeutic efficacy. This study proposes and applies a four-phase research framework designed to guide the development and assessment of AI-powered plush robots for social rehabilitation and education. Phase 1 involved semi-structured interviews with 13 ASD specialists to explore robot applications. Phase 2 tested initial usability with typically developing children (N = 10–15) through structured sessions. Phase 3 involved structured interaction sessions with children diagnosed with ASD (N = 6–8) to observe the robot’s potential for rehabilitation, observed by specialists and recorded on video. Finally, Phase 4 synthesized data via multidisciplinary triangulation. Results highlighted the importance of iterative, stakeholder-informed design, with experts emphasizing visual properties (color, texture), psychosocial aspects, and adjustable functions. The study identified key technical and psychological evaluation criteria, including engagement, emotional safety, and developmental alignment with ASD intervention models. Findings underscore the value of qualitative methodologies and phased testing in developing child-centered robotic tools. The research establishes a robust methodological framework and provides preliminary evidence for the potential of AI-powered plush robots to support personalized, ethically grounded interventions for children with ASD, though their therapeutic efficacy requires further longitudinal validation. This methodology bridges engineering innovation with psychological rigor, offering a template for future assistive technology research by prioritizing a rigorous, stakeholder-centered design process. Full article

Humans in a society generally tend to implicitly adhere to the shared social norms established within that culture. Robots operating in a dynamic environment shared with humans are also expected to behave socially to improve their interaction and enhance their likability among humans. Especially when moving into close proximity of their human partners, robots should convey perceived safety and intelligence. In this work, we model human proxemics as robot navigation costs, allowing the robot to exhibit avoidance behavior around humans or to initiate interactions when engagement is required. The proxemic model enhances robot navigation by incorporating human-aware behaviors, treating humans not as mere obstacles but as social agents with personal space preferences. The model of interaction positions estimates suitable locations relative to the target person for the robot to approach when an engagement occurs. Our evaluation on human–robot interaction data and simulation experiments demonstrates the effectiveness of the proposed models in guiding the robot’s avoidance and approaching behaviors toward humans. Full article

The field of haircutting robots is poised for a significant transformation, driven by advancements in artificial intelligence, mechatronics, and humanoid robotics. This perspective paper examines the emerging market for haircutting robots, propelled by decreasing hardware costs and a growing demand for automated grooming services. We review foundational technologies, including advanced hair modeling, real-time motion planning, and haptic feedback, and analyze their application in both teleoperated and fully autonomous systems. Key technical requirements and challenges in safety certification are discussed in detail. Furthermore, we explore how cutting-edge technologies like direct-drive systems, large language models, virtual reality, and big data collection can empower these robots to offer a human-like, personalized, and efficient experience. We propose a business model centered on supervised autonomy, which enables early commercialization and sets a path toward future scalability. This perspective paper provides a theoretical and technical framework for the future deployment and commercialization of haircutting robots, highlighting their potential to create a new sector in the automation industry. Full article

(1) Background: Alzheimer’s disease and related dementias (ADRD) are a major cause of mortality among older adults globally. The cognitive decline associated with ADRD often reduces individuals’ ability to live independently over time, increasing reliance on caregivers. Assistive and socially assistive robots offer a promising means of supporting independent living. This narrative review examined how older adults with ADRD, their caregivers, and healthcare providers perceive and experience interactions with robots. (2) Methods: Guided by the Population, Phenomenon of Interest, and Context (PICo) framework, five databases were searched. Sixteen studies met the inclusion criteria. Extracted data were summarized, and a convergent synthesis integrated qualitative and quantitative findings. (3) Results: Drawing on content analysis, the qualitative findings were organized into three domains: user perceptions and experiences, barriers to adoption, and suggestions for improvement. Quantitative results emphasized usability, usefulness, acceptance, satisfaction, feature preferences, and barriers. While most stakeholders viewed robots as beneficial, acceptance was shaped by factors such as design features, timing of introduction, familiarity with technology, and perceived need. (4) Conclusions: This review highlights priorities for future research and development, including personalization, ethical safeguards, and caregiver integration, to improve the acceptance and effectiveness of robot-assisted support for individuals with cognitive impairment. Full article