Search Results (240)

Wearable robots can improve human walking economy; however, their effectiveness depends on user adaptation to assistance. This study introduces a framework for real-time estimation of user adaptation that relies only on wearable sensor data during operation. Metabolic measurements were used solely to establish the ground truth adaptation curves for model training and validation but are not required for real-time inference. Five healthy adults completed six days of treadmill walking while wearing a soft hip exosuit that provided hip extension assistance. Thigh-mounted inertial measurement units recorded step timing and hip-angle trajectories, from which three variability-based features (step-frequency variability, maximum hip-flexion variability, and maximum hip-extension variability) were extracted. A Long Short-Term Memory (LSTM) model used these gait-variability inputs to estimate each user’s adaptation level relative to a metabolic cost benchmark obtained from respiratory gas analysis. Across sessions, the metabolic cost decreased by 9.0 ± 5.6% from Day 1 to Day 6 (p < 0.01) with a mean time constant of 202 ± 78 min, In contrast, the variability in step frequency, maximum hip flexion, and maximum hip extension decreased by 66.4 ± 6.8%, 37.9 ± 24.2%, and 42.8 ± 10.6%, respectively, indicating that these reductions were users’ progressive adaptation to the exosuit’s assistance. Under leave-one-subject-out (LOSO) evaluation across five participants, 59.2% of the model predictions fell within ±10 percentage points of the metabolic cost–based adaptation curve. These results suggest that simple kinematic variability measured with wearable sensors can track user adaptation and support practical approaches to real-time monitoring. Such capability can facilitate adaptive control and training protocols that personalize exosuit assistance. Full article

Artificial intelligence (AI) is rapidly reshaping gastrointestinal (GI) surgery by enhancing decision-making, intraoperative performance, and postoperative management. The integration of AI-driven systems is enabling more precise, data-informed, and personalized surgical interventions. This review provides a state-of-the-art overview of AI applications in GI surgery, organized into four key domains: surgical simulation, surgical computer vision, surgical data science, and surgical robot autonomy. A comprehensive narrative review of the literature was conducted, identifying relevant studies of technological developments in this field. In the domain of surgical simulation, AI enables virtual surgical planning and patient-specific digital twins for training and preoperative strategy. Surgical computer vision leverages AI to improve intraoperative scene understanding, anatomical segmentation, and workflow recognition. Surgical data science translates multimodal surgical data into predictive analytics and real-time decision support, enhancing safety and efficiency. Finally, surgical robot autonomy explores the progressive integration of AI for intelligent assistance and autonomous functions to augment human performance in minimally invasive and robotic procedures. Surgical AI has demonstrated significant potential across different domains, fostering precision, reproducibility, and personalization in GI surgery. Nevertheless, challenges remain in data quality, model generalizability, ethical governance, and clinical validation. Continued interdisciplinary collaboration will be crucial to translating AI from promising prototypes to routine, safe, and equitable surgical practice. Full article

Accurate gait-phase detection is essential for rehabilitation monitoring, prosthetic control, and human–robot interaction. Artificial intelligence supports continuous, personalized mobility assessment by extracting clinically meaningful patterns from wearable sensors. A richer view of gait dynamics can be achieved by integrating additional signals, including inertial, plantar flex, footswitch, and EMG data, leading to more accurate and informative gait analysis. Motivated by these needs, this study investigates discrete gait-phase recognition for the right leg using a multi-subject IMU dataset collected from lower-limb sensors. IMU recordings were segmented into 128-sample windows across 23 channels, and each window was flattened into a 2944-dimensional feature vector. To ensure reliable ground-truth labels, we developed an automatic relabeling pipeline incorporating heel-strike and toe-off detection, adaptive threshold tuning, and sensor fusion across sensor modalities. These windowed vectors were then used to train a comprehensive suite of machine learning models, including Random Forests, Extra Trees, k-Nearest Neighbors, XGBoost, and LightGBM. All models underwent systematic hyperparameter tuning, and their performance was assessed through k-fold cross-validation. The results demonstrate that tree-based ensemble models provide accurate and stable gait-phase classification with accuracy exceeding 97% across both test sets, underscoring their potential for future real-time gait analysis and lower-limb assistive technologies. Full article

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

Empathic behavior is increasingly incorporated into socially assistive robots, yet little is known about how older adults’ personality-based self-regulatory processes shape responses to such designs. The present study examined a recognition-based “mirror effect” framework of narcissistic self-regulation, referring to the ways individuals maintain a valued self-image through social feedback and acknowledgment. We focused on two core dimensions: narcissistic admiration, characterized by self-promotion and the pursuit of affirmation, and narcissistic rivalry, characterized by defensiveness, antagonism, and sensitivity to threat. Community-dwelling older adults (N = 527; M_age = 72.73) were randomly assigned to view a video of a socially assistive robot interacting in either an empathic or a cold manner. Participants reported their perceived recognition by the robot, defined as the subjective experience of feeling seen, acknowledged, and valued, as well as multiple robot evaluations (anthropomorphism, likability, perceived intelligence, safety, and intention to use). At the mean level, empathic robot behavior increased perceived recognition, anthropomorphism, and likability but did not improve perceived intelligence, safety, or intention to use. Conditional process analyses revealed that narcissistic admiration was positively associated with perceived recognition, which in turn predicted more favorable robot evaluations, regardless of robot behavior. In contrast, narcissistic rivalry showed a behavior-dependent pattern: rivalry was associated with reduced perceived recognition and less favorable evaluations primarily in the empathic condition, whereas this association reversed in the cold condition. Importantly, once perceived recognition and narcissistic traits were accounted for, the cold robot was evaluated as more intelligent, safer, and more desirable to use than the empathic robot. Studying these processes in older adults is theoretically and practically significant, as later life is marked by shifts in social roles, autonomy concerns, and sensitivity to interpersonal evaluation, which may alter how empathic technologies are experienced. Together, the findings identify perceived recognition as a central psychological mechanism linking personality and robot design and suggest that greater robotic empathy is not universally beneficial, particularly for users high in rivalry-related threat sensitivity. Full article

Background: Assistive technologies (ATs) for individuals with tetraplegia have evolved from mechanical aids to complex neurotechnological, digital, and psychosocial systems. However, the evidence base remains fragmented, with heterogeneous methodologies and limited integration across domains. This review synthesizes recent review-level evidence to clarify current trends, gaps, and directions in ATs for tetraplegia. Methods: A narrative review of reviews was conducted following the ANDJ checklist. PubMed and Scopus were searched for systematic, scoping, and narrative reviews addressing assistive technologies relevant to tetraplegia. After screening, de-duplication, and quality appraisal, 20 reviews were included and synthesized narratively. Results: The included reviews clustered into four main domains: neural and regenerative interfaces, motor and biomechanical assistive systems, digital and adaptive rehabilitation ecosystems, and psychosocial and integrative frameworks. Across domains, evidence highlights a shift toward personalized, adaptive, and interoperable systems, supported by neurotechnologies, robotics, mobile health, and virtual reality. Common limitations include heterogeneous outcome measures, scarcity of longitudinal evidence, limited system interoperability, and persistent inequities in access and adoption. Emerging applications of artificial intelligence support adaptive control, monitoring, and personalization, though robust clinical validation remains limited. Conclusions: This synthesis provides a structured overview of review-level evidence on assistive technologies for tetraplegia. The findings underscore the need for coordinated, multidisciplinary approaches and more rigorous, longitudinal evaluation to support the development of inclusive, human-centered, and interoperable assistive ecosystems. Full article

Artificial intelligence (AI) is rapidly reshaping thoracic surgery, advancing from decision support to the threshold of autonomous intervention. AI-driven technologies—including machine learning (ML), deep learning (DL), and computer vision—have demonstrated significant improvements in diagnostic accuracy, surgical planning, intraoperative navigation, and postoperative outcome prediction. In lung cancer and thoracic oncology, AI enhances imaging analysis, histopathological classification, and risk stratification, supporting multidisciplinary decision-making and personalized therapy. Robotic-assisted and AI-guided systems are optimizing surgical precision and workflow efficiency, while real-time decision-support tools and augmented reality are improving intraoperative safety. Despite these advances, widespread adoption is limited by challenges in algorithmic bias, data integration, regulatory approval, and ethical transparency. The literature emphasizes the need for multicenter validation, explainable AI, and robust governance frameworks to ensure safe and effective clinical integration. Future research should focus on digital twin technology, federated learning, and transparent AI outputs to further enhance reliability and accessibility. AI is poised to transform thoracic surgery, but responsible implementation and ongoing evaluation are essential for realizing its full potential. The aim of this review is to evaluate and synthesize the current landscape of artificial intelligence (AI) applications across the thoracic surgical pathway, from preoperative decision-support to intraoperative guidance and emerging autonomous interventions. Full article

Background/Objectives: Socially assistive robots effectively support elderly care when they incorporate personalization, person-centered principles, rich interactions, and careful role setting with psychosocial alignment. Hyodol, a socially assistive robot designed for elderly people, embodies a grandchild’s persona, emulating the grandparent–grandchild relationship. Based on the behavioral activation principles and a human-centered approach, this robot continuously supports users’ emotional well-being, health management, and daily routines. Methods: The current study evaluated Hyodol’s impact on depressive symptoms and other quality of life factors among older adults living in medically underserved areas. A total of 278 participants were assessed for depressive symptoms, loneliness, medication adherence, and user acceptance. Results: After six months of use, participants showed significant reductions in overall depressive symptoms, with a 45% decrease in the proportion of individuals at high risk of depression. Significant improvements were also observed in loneliness and medication adherence. Participants reported high levels of user acceptance and satisfaction, exceeding 70% of the total score. Participants who engaged more frequently in free chat with Hyodol showed greater improvements in depressive symptoms. Conclusions: These results highlight Hyodol’s potential as a promising tool for enhancing mental healthcare and overall well-being in this population. This at-home mental-healthcare framework can complement primary care and, if its effects are confirmed in controlled trials, could contribute to reducing healthcare burden and preventing the onset and escalation of depressive symptoms. Full article

Background: As ageing populations grow, the prevalence of dementia and pre-dementia conditions is rising. Emerging approaches to neurorehabilitation emphasize not only performance-based outcomes but also holistic, experiential, and person-centred aspects of care. The extended mind thesis further highlights the potential role of external tools in supporting impaired cognitive functions. Within this ecological and experiential perspective, Social Assistive Robotics (SAR) may offer a multidimensional approach to address cognitive, emotional, and social needs in neurocognitive disorders. Objective: To synthesize current evidence on the effects of robotic interventions within an enactive framework integrating mind, body, environment, and technology. Methods: A systematic search was conducted in PubMed, Ovid Medline, Scopus, ScienceDirect, Springer, Wiley, IEEE Xplore, ACM Digital Library, and the Cochrane Library. Due to heterogeneity among included studies, an umbrella review was performed using vote-counting by direction of effect as a non-quantitative synthesis method. Methodological rigour followed JBI and Cochrane guidelines. Results: Sixteen reviews were included. The strongest and most consistent benefits emerged for affective outcomes, particularly emotional response and social interaction p = 0.007 (two-sided). Conversely, outcomes related to cognition, anxiety, agitation, depression, and quality of life showed mixed or non-significant effects, while neuropsychiatric symptoms demonstrated no benefit. Conclusions: Discrepancies across reviews seem driven by methodological limitations in primary studies, limiting interpretability. The strength of this umbrella review lies in identifying systematic gaps that can guide future research. With stronger evidence, integrating SAR into experiential neurorehabilitation may offer a promising avenue for holistic, ecologically grounded care that extends beyond traditional task-based performance. Trial Registration: PROSPERO CRD420251165419. Full article

Background: Recent trials, including JCOG0802/WJOG4607L and CALGB140503, have confirmed the oncological adequacy of segmentectomy for early-stage non-small-cell lung cancer (NSCLC). This shift emphasizes the preservation of pulmonary function and minimal invasiveness. Robot-assisted thoracic surgery (RATS) offers enhanced anatomical precision and potentially improves segmentectomy outcomes. Methods: We reviewed the current evidence comparing sublobar resection and lobectomy for early-stage NSCLC, focusing on RATS segmentectomy. Clinical trials, perioperative and long-term outcomes, technical innovations, and patient selection criteria were analyzed. Comparative data among RATS, video-assisted thoracoscopic surgery (VATS), and open approaches were synthesized, including the emerging roles of AI and 3D imaging. Results: Segmentectomy yields survival outcomes equivalent or superior to lobectomy for stage IA peripheral NSCLC ≤2 cm, with better pulmonary function despite higher locoregional recurrence. RATS enhances visualization, dexterity, and ergonomics, thereby enabling precise dissection and lymph node assessment. Compared to VATS and open surgery, RATS shows lower conversion rates, reduced pain, and comparable oncological control. Innovations, such as indocyanine green imaging, 3D modeling, and AI-guided navigation, support margin accuracy and personalized care. Conclusions: Segmentectomy has redefined the surgical standards for early-stage NSCLC. RATS maximizes the minimally invasive benefits by combining oncological safety and functional preservation. Its technical precision facilitates complex resections and integration with digital planning tools to advance personalized thoracic surgery. RATS represents the next evolution of minimally invasive thoracic surgery, redefining the balance between oncological safety and functional preservation in early-stage NSCLC. Full article

Background/Objectives: Some personalized alignment (PA) concepts have been described with symmetrical gaps in extension and flexion. However, laxity in native knees was significantly greater laterally than medially with respect to both extension and flexion. We hypothesized that a personalized alignment can restore the native knee alignment, keep a satisfying patellar tracking, and obtain physiological ligament balancing, that is, a symmetric gap in extension and an asymmetric gap in flexion. We aimed to assess: (1) the postoperative alignment of TKA and postoperative patellar tracking (primary outcome); (2) the ligament balancing at the end of the surgery; and (3) clinical outcomes and complication rates. Methods: In this single-center, retrospective case series, we evaluated 45 patients in a consecutive series who underwent robotic-assisted primary TKA using PA between January and September 2020 with a minimum follow-up of 5 years. Complication was defined as grade ≥3 according to the Clavien-Dindo classification. Data assessed were: TKA alignment and implant positioning on postoperative radiographs, patellar tracking on the merchant view, and ligament balancing in extension and flexion upon completion of surgery. Results: Mean follow-up was 62.1 ± 2.5 months. The postoperative mean HKA angle was 177.4° ± 2.2. The medial distal femoral angle was restored (91.1° ± 1.5 postoperatively versus 91.3° ± 2). A total of four TKAs had a patellar tilt superior to 5° (8.9%). No significant difference was found in the medial gap laxity—both in extension and in flexion—and the lateral gap laxity in extension. The lateral gap laxity in flexion was significantly higher than extension or medial gap laxity (+2.9 mm). One patient was readmitted for delayed wound healing. Average improvements in Knee Society knee and function scores were 55.86 and 51.84 points, respectively. Conclusions: This personalized alignment technique using anatomo-functional implant positioning allowed restoration of native knee alignment with a “safe zone” (3° varus/valgus) for the tibial implant, maintained satisfying patellar tracking, and restituted the asymmetrical gap laxity in flexion with a higher laxity in the lateral compartment. Being the longest system-specific study to date, the results are encouraging at 5 years with no major complications. However, longer follow-up will be required to confirm the use of this technique. Full article

Background: Multimodal artificial intelligence (MMAI) is transforming urological oncology by enabling the seamless integration of diverse data sources, including imaging, clinical records and robotic telemetry to facilitate patient-specific decision-making. Methods: This narrative review summarizes the current developments, applications, opportunities and risks of multimodal AI systems throughout the entire perioperative process in uro-oncologic surgery. Results: MMAI demonstrates quantifiable benefits across the entire perioperative pathway. Preoperatively, it improves diagnostics and surgical planning via multimodal data fusion. Intraoperatively, AI-assisted systems provide real-time context-based decision support, risk prediction and skill assessment within the operating theater. Postoperatively, MMAI facilitates automated documentation, early complication detection and personalized follow-up. Generative AI further revolutionizes surgical training through adaptive feedback and simulations. However, critical limitations must be addressed, including data bias, the barrier of closed robotic platforms, insufficient model validation, data security issues, hallucinations and ethical concerns regarding liability and transparency. Conclusions: MMAI significantly enhances the precision, efficiency and patient-centeredness of uro-oncological care. To ensure safe and widespread implementation, resolving the technical and regulatory barriers to real-time integration into robotic platforms is paramount. This must be coupled with standardized quality controls, transparent decision-making processes and responsible integration that fully preserves physician autonomy. Full article

Exoskeletons offer promising solutions for enhancing human mobility; however, personalizing assistance parameters to optimize physiological outcomes remains challenging. Human-in-the-loop (HIL) optimization has emerged as an effective strategy for tailoring device control, often using electromyography (EMG) as a real-time proxy for metabolic cost. This study simulates HIL optimization using surrogate models built from the average root mean square of the muscles’ activations (EMG-RMS) derived from treadmill walking trials with a robotic waist tether. Nine surrogate models were evaluated for prediction accuracy, including gradient boosting (GB), random forest, support vector regression, and Gaussian process variants. Seven global optimization algorithms were compared based on convergence time, EMG-RMS at optimum, and efficiency metrics. GB achieved the highest predictive accuracy (1.57% RAEP). Among optimizers, the gravitational search algorithm (GSA) produced the lowest EMG-RMS value (0.17 normalized units) and the fastest convergence (0.32 s), while particle swarm optimization (PSO) achieved 0.36 EMG-RMS in 1.61 s. These findings demonstrate the value of EMG-based simulation frameworks in guiding algorithm selection for HIL optimization, ultimately reducing the experimental burden in developing personalized exoskeleton assistance strategies. Full article

Background/Objectives: Urinary incontinence (UI) remains a common and distressing complication following radical prostatectomy (RP). This prospective observational study aimed to assess the effectiveness of structured pelvic floor rehabilitation and to identify clinical and surgical predictors of continence recovery. Methods: A total of 182 patients undergoing RP received standardized physiotherapist-guided pelvic floor muscle training (PFMT), including supervised sessions before and after surgery, as well as individualized home exercise programs. UI severity was evaluated using a 1 h pad test and a four-level UI stage classification at three time points. The primary outcomes were changes in UI stage and the achievement of full continence, defined as a pad test result ≤2 g. Results: Following three rehabilitation sessions, 80.2% of patients regained full continence. Preoperative PFMT (β = −1.27, p = 0.0061) and shorter time to rehabilitation (β = −0.04, p = 0.0026) were associated with greater improvement in continence outcomes. Patients treated with robot-assisted RP showed a higher probability of continence recovery compared to those undergoing laparoscopic RP, particularly in the presence of moderate to severe baseline incontinence. Higher baseline urinary leakage significantly decreased the odds of treatment success (β = −0.01, p = 0.0001). ISUP grade and extraprostatic extension were not independently associated with outcomes. Conclusions: Despite the absence of a control group, this study demonstrates the effectiveness of structured and personalized pelvic floor rehabilitation in improving post-RP continence. Early initiation and preoperative training should be prioritized to optimize recovery in routine clinical practice. Full article

Background: Stroke leads to lasting disability by disrupting the connectivity of functional brain networks. Although several rehabilitation methods are promising, our full understanding of how these strategies restore network function is still limited. Here, we map how non-invasive brain stimulation (NIBS), brain–computer interface (BCI)/neurofeedback, virtual reality (VR), and robot-assisted therapy restore connectivity within the sensorimotor network (SMN), default mode network (DMN), and salience network, and we contextualize these effects within the known temporal evolution of post-stroke motor network reorganization. Methods: This scoping review adhered to PRISMA guidelines and searched PubMed, Cochrane, and Medline from January 2015 to January 2025 for clinical trials focused on stroke rehabilitation with functional connectivity outcomes. Included studies used conventional therapy, neuromodulation, or feedback-based interventions. Results: Twenty-three studies fulfilled the inclusion criteria, covering interventions like robotic training, transcranial stimulation (tDCS/TMS), brain–computer interfaces, virtual reality, and cognitive training. Motor impairments were linked to disrupted interhemispheric sensorimotor connectivity, while cognitive issues reflected changes in frontoparietal and default mode networks. Combining neuromodulation with feedback-based methods showed better network recovery than standard therapy alone, with clinical improvements closely associated with connectivity alterations. Conclusions: Effective stroke rehabilitation depends on targeting specific disrupted networks through various modalities. Robotic interventions focus on restoring structural motor pathways, feedback-enhanced methods improve temporal synchronization, and cognitive training aims to enhance higher-order network integration. Future research should work toward standardizing connectivity assessment protocols and conducting multicenter trials. This will help develop evidence-based, network-focused rehabilitation guidelines that effectively translate mechanistic insights into personalized clinical treatments. Full article