Search Results (192)

Access to practice opportunities for history-taking in undergraduate medical education can be resource-limited. Large language models are a potential avenue to address this. This study sought to characterize changes in learner self-reported confidence with history-taking before and after a simulation with an LLM-based patient and understand learner experience with and the acceptability of virtual LLM-based patients. This was a multi-method study conducted at McMaster University. Simulations were facilitated with the OSCEai tool. Data was collected through surveys with a Likert scale and open-ended questions and semi-structured interviews. A total of 24 participants generated 93 survey responses and 17 interviews. Overall, participants reported a 14.6% increase in comfort with history-taking. Strengths included its flexibility, accessibility, detailed feedback, and ability to provide a judgement-free space to practice. Limitations included its lower fidelity compared to standardized patients and at times repetitive and less clinically relevant feedback as compared to preceptors. It was overall viewed best as a supplement rather than a replacement for standardized patients. In conclusion, LLM-based virtual patients were feasible and valued as an adjunct tool. They can support scalable, personalized practice. Future work is needed to understand objective metrics of improvement and to design curricular strategies for integration. Full article

A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as ‘Watch_TV’, ‘Sleep’, ‘Work_On_Computer’, and ‘Cook_Dinner’, is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models—XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)—are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation. Full article

Background/Objectives: Outpatient surgery enhances patient comfort while reduces surgical wait times and healthcare costs compared to inpatient procedures. This study evaluates the individual feasibility of performing transoral trans muscular cordectomies with microelectrodes (TOMES) on an outpatient basis. Methods: This observational study analyses TOMES types III, IV, and V cordectomies performed from January 2002 to December 2023. Key metrics include patient demographics, procedural details, incidence of bleeding, anticoagulation and other comorbidities. Results: Of the 143 procedures, 127 were cancer-related, while 16 were due to bilateral vocal cord paralysis. The average age was 65, with a predominantly male cohort (92%). Postoperative hemorrhage occurred in four cases, primarily among oncological patients, but there was no correlation with anticoagulation therapy. A personalized predictive model for bleeding risk was developed based on patient-specific characteristics and observed outcomes. Additionally, performing the procedure on an outpatient basis decreased healthcare costs and wait times for patients with T1/T2 glottic carcinoma. Conclusions: The findings indicate that TOMES type III or higher cordectomies can be safely performed on an outpatient basis, through the use of a personalized predictive model for each case and with appropriate postoperative monitoring. This approach has the potential to lower healthcare costs and improve patient quality of life through individualized assessments and structured risk analysis. Full article

Background: There is an emerging need for new scalable behavioral assays, i.e., assays that are feasible to administer from the comfort of the person’s home, with ease and at higher frequency than clinical visits or visits to laboratory settings can afford us today. This need poses several challenges which we address in this work along with scalable solutions for behavioral data acquisition and analyses aimed at diversifying various populations under study here and to encourage citizen-driven participatory models of research and clinical practices. Methods: Our methods are centered on the biophysical fluctuations unique to the person and on the characterization of behavioral states using standardized biorhythmic time series data (from kinematic, electrocardiographic, voice, and video-based tools) in naturalistic settings, outside a laboratory environment. The methods are illustrated with three representative studies (58 participants, 8–70 years old, 34 males, 24 females). Data is presented across the nervous systems under a proposed functional taxonomy that permits data organization according to nervous systems’ maturation and decline levels. These methods can be applied to various research programs ranging from clinical trials at home, to remote pedagogical settings. They are aimed at creating new standardized biometric scales to screen and diagnose neurological disorders across the human lifespan. Results: Using this remote data collection system under our new unifying statistical platform for individualized behavioral analysis, we characterize the digital ranges of biophysical signals of neurotypical participants and report departure from normative ranges in neurodevelopmental and neurodegenerative disorders. Each study provides parameter spaces with self-emerging clusters whereby data points corresponding to a cluster are probability distribution parameters automatically classifying participants into different continuous Gamma probability distribution families. Non-parametric analysis reveals significant differences in distributions’ shape and scale (p < 0.01). Data reduction is realizable from full probability distribution families to a single parameter, the Gamma scale, amenable to represent each participant within each subclass, and each cluster of similar participants within each cohort. We report on data integration from stochastic analyses that serve to differentiate participants and propose new ways to highly scale our research, education, and clinical practices. Conclusions: This work highlights important methodological and analytical techniques for developing personalized and scalable biometrics across various populations outside a laboratory setting. Full article

This paper introduces UTHECA_USE, a dataset of 989 observations collected in Seville, Spain (2023–2025), integrating microclimatic, personal, and urban morphological data. It comprises 55 variables, including in situ measurements of air and globe temperatures, humidity, wind speed, derived indices such as the Universal Thermal Climate Index (UTCI), demographic and physiological participant data, subjective thermal perception, and detailed urban form characteristics. The surface temperature data of urban materials are included in a subset. The dataset is openly accessible under a permissive license, and this data descriptor documents the collection methods, calibration, survey design, and data processing to ensure reproducibility and transparency. The UTHECA project aims to develop a more accurate and adaptive outdoor thermal comfort (OTC) assessment model to guide effective, inclusive urban strategies to improve human thermal perception and climate resilience. UTHECA_USE facilitates research on outdoor thermal comfort and urban microclimates, supporting diverse analyses linking human perception, environmental conditions, and urban morphology. Full article

This study aims to develop a personal comfort model driven by real-time electroencephalogram (EEG) signals for constructing built environments customized to individual emotional states and preferences. EEG signals from a single subject were collected at regular intervals under controlled environmental conditions—temperature, humidity, and illumination. Real-time deep learning methods processed the sensor data, enabling effective prediction of the user’s preferred conditions. Model evaluation showed reliable predictions on the personal dataset, allowing for optimized lighting that enhanced concentration and reduced stress. These findings indicate that EEG can inform personalized environmental modifications. This integration of EEG and deep learning provides objective, precise comfort assessment and supports immediate environmental adaptation. Full article

Despite growing global interest in customer experience management, limited research has systematically integrated both quantitative and qualitative approaches to identify service performance gaps and formulate strategic responses in airport settings. This study addresses this gap by examining how Bangkok Suvarnabhumi Airport can enhance its passenger experience through empirical analysis and international benchmarking. The research investigates the alignment between international passengers’ expectations and their actual experiences across seven key airport touchpoints: check-in, security, immigration, boarding, accessibility, facilities, and retail areas. A structured survey of 474 outbound international passengers was conducted between June and July 2024 using purposive sampling. Quantitative data were analyzed using Importance–Performance Analysis (IPA) to evaluate six experience components: affective, cognitive, sensory, conative, physical, and social identity. The IPA results revealed notable service gaps, particularly in conative engagement, physical comfort, and social identity, which were subsequently prioritized for strategic improvement. To validate and enrich strategy formulation, qualitative benchmarking was conducted through semi-structured interviews with ten executives at Hong Kong International Airport, a global leader in passenger experience management. The resulting strategic framework, termed the SCOPE strategy, integrates passenger insights with expert perspectives to guide the design of seamless, personalized, and empathy-driven airport experiences. Theoretically, this study contributes a validated six-component passenger experience model and demonstrates the utility of IPA in service design for complex transport hubs. Practically, it offers airport authorities a replicable, data-informed roadmap for enhancing emotional engagement, service consistency, and cross-stakeholder collaboration in similarly scaled international airports. Full article

Background/Objectives: To compare patient-reported outcome measures (PROMs) between digital and conventional impression techniques for implant-supported fixed dental prostheses (iFDPs). Methods: A systematic literature search was conducted in PubMed, Embase, Scopus, and the Cochrane Library databases up to June 2025, following PRISMA guidelines. Human clinical studies reporting PROMs between digital and conventional impression techniques for iFDPs were included. Studies using structured, but not necessarily validated, questionnaires were eligible. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. Where possible, meta-analyses were conducted using a random-effects model to pool comparable outcomes across studies using mean differences (MD) or standardized mean differences (SMD) with 95% confidence intervals (CIs). Results: Out of 1784 records screened, eighteen studies were included. Most studies showed that digital impressions were associated with higher patient satisfaction, compared to conventional impressions. Ten studies contributed data to at least one outcome; pooled analyses included the following: overall satisfaction (k = 5), comfort (k = 7), gagging/nausea (k = 5), esthetic satisfaction (k = 2), unpleasant taste (k = 5), anxiety (k = 5), discomfort (k = 2), pain (k = 5), and overall discomfort (k = 5). Digital impressions were significantly favored (p < 0.05) for anxiety (MD = 13.3, 95% CI: −22 to −4.5), nausea (MD = −26.4, 95% CI −46.8 to −6.0), bad taste (MD = −34.8, 95% CI −58.3 to −11.3), discomfort (SMD = −2.24, 95% CI −3.51 to −0.98), comfort (SMD = 1.77, 95% CI: 0.60 to 2.94), perceived procedure time (SMD = 0.96; 95% CI 0.29 to 1.62), and overall satisfaction (SMD = 0.55; 95% CI 0.01 to 1.09). No statistically significant differences were found for pain or esthetic evaluation. Substantial between-study heterogeneity was observed among the included studies. Conclusions: Current evidence indicates that digital impression workflows enhance the overall patient experience for implant-supported fixed restorations, especially in domains linked to comfort and procedural efficiency. These findings support PROM-informed personalization of impression workflows: screening for gagging, anxiety, or intolerance to impression materials could guide patient-tailored use of intraoral scanning while acknowledging no consistent advantage for pain or esthetic perception. Full article

Limb amputation causes significant challenges for patients in achieving effective mobility and functionality through prosthetic limbs. The prosthetic socket plays a pivotal role in the success of rehabilitation. This review explores the current advancements in prosthetic socket design and fabrication, focusing on traditional techniques like casting and lamination, and emerging technologies such as 3D printing and computer-aided design (CAD). By comparing these methods, this review highlights the advantages, limitations, and suitability for different clinical needs. This article discusses the importance of pressure distribution in socket design, emphasizing the need to relieve pressure in sensitive areas to prevent skin complications. It also examines the materials used in socket fabrication, from high-density polymers to advanced composites, assessing their impact on patient comfort and prosthetic performance. Additionally, we discuss the challenges practitioners face in prosthetic care, particularly in low-resource settings, and propose potential solutions through innovative techniques and materials. Advancements in computational modeling improved socket design and validation, enhancing patient comfort and improving the overall biomechanical interaction between the prosthesis and the user. The manuscript concludes by identifying future research opportunities, particularly in personalized prosthetic design and the integration of smart materials, to further enhance the comfort, functionality, and accessibility of prosthetic sockets. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

The improvement of comfort in the human–robot interaction for care recipients is a significant challenge in the development of nursing robots. The existing methods for enhancing comfort largely depend on subjective comfort questionnaires, which are prone to unavoidable errors. Additionally, traditional passive movement control approaches lack the ability to adapt and effectively improve care recipient comfort. To address these problems, this paper proposes an active, personalized intelligent control method based on neural networks. A muscle activation prediction model is established for the piggyback transfer robot, enabling dynamic adjustments during the care process to improve human comfort. Initially, a kinematic analysis of the piggyback transfer robot is conducted to determine the optimal back-carrying trajectory. Experiments were carried out to measure human–robot contact forces, chest holder rotation angles, and muscle activation levels. Subsequently, an Online Sequential Extreme Learning Machine (OS-ELM) algorithm is used to train a predictive model. The model takes the contact forces and chest holder rotation angle as inputs, while outputting the latissimus dorsi muscle activation levels. The Genetic Algorithm (GA) is then employed to dynamically adjust the chest holder’s rotation angle to minimize the difference between actual muscle activation and the comfort threshold. Comparative experiments demonstrate that the proposed ELM-GA-based active control method effectively enhances comfort during the piggyback transfer process, as evidenced by both subjective feedback and objective measurements of muscle activation. Full article

Work–life balance (WLB) is central to sustainable social and economic development, as reflected in the UN Sustainable Development Goals 3, 5, and 8. The purpose of this article is to identify and examine the key organisational and personal factors influencing the perceived work–life balance of employees in rural areas. The theoretical framework is grounded in three complementary approaches: the job demands–resources (JD-R) model, spillover theory, and boundary theory. Together, they offer a comprehensive perspective on role dynamics in the context of limited resources, technostress, and family-related tensions. The study was conducted on a sample of 700 rural employees in Poland, predominantly women (60.6%), with the majority aged 35–55 years (53.0%). Data were collected via a structured questionnaire and analysed using an exploratory approach based on regression trees (CART), which are effective in identifying latent and multidimensional relationships. The findings highlight the mechanisms underlying WLB disruptions in rural contexts and pinpoint areas for intervention through public and organisational policies aimed at supporting employee well-being. The most influential factors were workplace comfort, work flexibility, time autonomy, and employee age. Notably, younger employees require better working conditions than older ones to achieve similar WLB levels. The CART analysis also indicates that some disadvantages, such as low workplace comfort, can be mitigated by more flexible work schedules. Employers should therefore provide multidimensional support through complementary measures, monitor job demands, and educate employees on the effective use of available resources. Full article

While personal thermal comfort is critical for well-being and productivity, it is often overlooked by traditional building management systems that rely on uniform settings. Modern data-driven approaches often fail to capture the complex interactions between various data streams. This pilot study introduces a high-accuracy, interpretable framework for thermal comfort classification, designed to identify the most significant predictors from a comprehensive suite of environmental, physiological, and anthropometric data in a controlled group of young adults. Initially, an XGBoost model using the full 24-feature dataset achieved the best performance at 91% accuracy. However, after using SHAP analysis to identify and select the most influential features, the performance of our ensemble models improved significantly; notably, a Random Forest model’s accuracy rose from 90% to 94%. Our analysis confirmed that for this homogeneous cohort, environmental parameters—specifically temperature, humidity, and CO₂—were the dominant predictors of thermal comfort. The primary strength of this methodology lies in its ability to create a transparent pipeline that objectively identifies the most critical comfort drivers for a given population, forming a crucial evidence base for model design. The analysis also revealed that the predictive value of heart rate variability (HRV) diminished when richer physiological data, such as diastolic blood pressure, were included. For final validation, the optimized Random Forest model, using only the top 10 features, was tested on a hold-out set of 100 samples, achieving a final accuracy of 95% and an F1-score of 0.939, with all misclassifications occurring only between adjacent comfort levels. These findings establish a validated methodology for creating effective, context-aware comfort models that can be embedded into intelligent building management systems. Such adaptive systems enable a shift from static climate control to dynamic, user-centric environments, laying the critical groundwork for future personalized systems while enhancing occupant well-being and offering significant energy savings. Full article

Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances remains a significant challenge, particularly in active-assist modes. This study presents a human-in-the-loop control architecture for a pediatric lower-limb exoskeleton, combining outer-loop admittance control with robust inner-loop trajectory tracking via a non-singular terminal sliding-mode (NSTSM) controller. Designed for active-assist gait rehabilitation in children aged 8–12 years, the exoskeleton dynamically responds to user interaction forces while ensuring finite-time convergence under system uncertainties. To enhance adaptability, we augment the inner-loop control with a twin delayed deep deterministic policy gradient (TD3) reinforcement learning framework. The actor–critic RL agent tunes NSTSM gains in real-time, enabling personalized model-free adaptation to subject-specific gait dynamics and external disturbances. The numerical simulations show improved trajectory tracking, with RMSE reductions of 27.82% (hip) and 5.43% (knee), and IAE improvements of 40.85% and 10.20%, respectively, over the baseline NSTSM controller. The proposed approach also reduced the peak interaction torques across all the joints, suggesting more compliant and comfortable assistance for users. While minor degradation is observed at the ankle joint, the TD3-NSTSM controller demonstrates improved responsiveness and stability, particularly in high-load joints. This research contributes to advancing pediatric gait rehabilitation using RL-enhanced control, offering improved mobility support and adaptive rehabilitation outcomes. Full article

Obstructive sleep apnea (OSA) is a prevalent and heterogeneous sleep disorder associated with significant health and societal burdens. While continuous positive airway pressure (CPAP) remains the gold standard treatment, its limitations in adherence and patient tolerance have highlighted the need for alternative therapies. Positional therapy (PT), which targets apneas that occur predominantly in the supine position, has emerged as a promising option for individuals with positional OSA (POSA). This narrative review synthesizes the current literature on PT, examining its clinical indications, typologies, comparative efficacy with CPAP, oral appliances, and hypoglossal nerve stimulation, as well as data on adherence and barriers to long-term use. Traditional methods such as the tennis ball technique have largely been replaced by modern vibrotactile devices, which demonstrate improved comfort, adherence, and comparable short-term outcomes in selected POSA subjects. While PT remains inferior to CPAP in reducing overall AHI and oxygen desaturation, it performs favorably in terms of mean disease alleviation (MDA) and sleep continuity. Importantly, treatment effectiveness is influenced by both anatomical and non-anatomical traits, underscoring the need for accurate phenotyping and individualized care. PT should be considered within a broader patient-centered model that incorporates preferences, lifestyle, and motivational factors. Further research is needed to validate long-term efficacy, optimize selection criteria, and integrate PT into personalized OSA management strategies. Full article