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Embolic stroke during cardiopulmonary bypass (CPB) is strongly influenced by cannula induced flow disturbances that govern emboli transport and aortic wall loading. This study quantifies how aortic cannula orientation affects embolic distribution and atherosclerotic plaque disruption risk across patient specific, age-dependent aortic anatomies under clinical CPB conditions. A validated computational fluid dynamics and Lagrangian particle tracking (CFD-LPT) framework was applied to four patient-specific aortic models representing pediatric, adolescent, adult, and geriatric anatomies. Two clinically relevant cannula orientations: perpendicular (90 deg) and angled (30 deg), were evaluated under varying blood viscosities (1.5 to 3.5 cP) and embolus sizes (0.5 to 2.5 mm). Aortic branch exit percentage, wall pressure, and wall shear stress (WSS) were quantified. The 30 deg angled cannula reduced embolic transport into the aortic branches by 18 to 50 percent compared with perpendicular cannulation, with the largest reduction observed in the geriatric model. Perpendicular cannulation produced concentrated jet impingement, resulting in significantly elevated posterior wall pressure (24 perc

Background: LLMs enable patient-facing conversational agents, creating a pathway toward digital twins that capture older adults' lived experiences and behavioral responses across time. A central barrier is personality drift -- inconsistent trait expression across repeated interactions -- which undermines reliability of generated trajectories and intervention-response simulation in geriatric care. Objective: To develop ELDER-SIM, a multi-role elderly-care conversational platform for building personality-stable digital twin agents, and to propose a psychometric validation framework for quantifying personality consistency in LLM-based agents. Methods: ELDER-SIM was implemented via n8n workflow orchestration with local LLM inference (Ollama/vLLM), integrating (1) Big Five (OCEAN) trait specifications, (2) a Cognitive Conceptualization Diagram (CCD) grounded in Beck's CBT framework, and (3) a MySQL-based long-term memory module. Ablation studies across four conditions -- Baseline, +Memory, +CCD, and +LoRA (fine-tuned on 19,717 instruction pairs from CHARLS) -- were evaluated via Cronbach's $α$, ICC, and role discrimination accuracy. Results: Reliability was acceptable to excellent acros

Physical activity during hip fracture rehabilitation is essential for mitigating long-term functional decline in geriatric patients. However, it is rarely quantified in clinical practice. Existing continuous monitoring systems with commercially available wearable activity trackers are typically developed in middle-aged adults and therefore perform unreliably in older adults with slower and more variable gait patterns. This study aimed to develop a robust human activity recognition (HAR) system to improve continuous physical activity recognition in the context of hip fracture rehabilitation. 24 healthy older adults aged over 80 years were included to perform activities of daily living (walking, standing, sitting, lying down, and postural transfers) under simulated free-living conditions for 75 minutes while wearing two accelerometers positioned on the lower back and anterior upper thigh. Model robustness was evaluated using leave-one-subject-out cross-validation. The synthetic data demonstrated potential to improve generalization across participants. The resulting feature intervention model (FIM), aided by synthetic data guidance, achieved reliable activity recognition with mean F1-

To overcome the limitations of manual administrative coding in geriatric Cardiovascular Risk Management, this study introduces an automated classification framework leveraging unstructured Electronic Health Records (EHRs). Using a dataset of 3,482 patients, we benchmarked three distinct modeling paradigms on longitudinal Dutch clinical narratives: classical machine learning baselines, specialized deep learning architectures optimized for large-context sequences, and general-purpose generative Large Language Models (LLMs) in a zero-shot setting. Additionally, we evaluated a late fusion strategy to integrate unstructured text with structured medication embeddings and anthropometric data. Our analysis reveals that the custom Transformer architecture outperforms both traditional methods and generative \acs{llm}s, achieving the highest F1-scores and Matthews Correlation Coefficients. These findings underscore the critical role of specialized hierarchical attention mechanisms in capturing long-range dependencies within medical texts, presenting a robust, automated alternative to manual workflows for clinical risk stratification.

The need to improve geriatric care quality presents a challenge that requires insights from stakeholders. While simulated trainings can boost competencies, extracting meaningful insights from these practices to enhance simulation effectiveness remains a challenge. In this study, we introduce Multimodal Epistemic Network Analysis (MENA), a novel framework for analyzing caregiver attitudes and emotions in an Augmented Reality setting and exploring how the awareness of a virtual geriatric patient (VGP) impacts these aspects. MENA enhances the capabilities of Epistemic Network Analysis by detecting positive emotions, enabling visualization and analysis of complex relationships between caregiving competencies and emotions in dynamic caregiving practices. The framework provides visual representations that demonstrate how participants provided more supportive care and engaged more effectively in person-centered caregiving with aware VGP. This method could be applicable in any setting that depends on dynamic interpersonal interactions, as it visualizes connections between key elements using network graphs and enables the direct comparison of multiple networks, thereby broadening its implic

The increasing global aging population has intensified the demand for reliable health monitoring systems, particularly those capable of detecting critical events such as falls among elderly individuals. Traditional fall detection approaches relying on single-modality acceleration data suffer from high false alarm rates, while conventional machine learning methods require extensive hand-crafted feature engineering. This paper proposes a novel multi-modal deep learning framework, MultiModalFallDetector, designed for real-time elderly fall detection using wearable sensors. Our approach integrates multiple innovations: a multi-scale CNN-based feature extractor capturing motion dynamics at varying temporal resolutions; fusion of tri-axial accelerometer, gyroscope, and four-channel physiological signals; incorporation of a multi-head self-attention mechanism for dynamic temporal weighting; adoption of Focal Loss to mitigate severe class imbalance; introduction of an auxiliary activity classification task for regularization; and implementation of transfer learning from UCI HAR to SisFall dataset. Extensive experiments on the SisFall dataset, which includes real-world simulated fall trials

This article describes the implementation of a technological solution aimed at improving the recording of physiological signals in the elderly population residing in geriatric facilities. The developed system consists of a smart device equipped with sensors for body temperature, heart rate, and blood oxygen levels. This device establishes an Internet connection to transmit data to a cloud-based platform for storage. Within this platform, a dashboard has been created to visualize real-time values captured by the sensors, along with additional functionalities such as user management and the configuration of personalized alerts, which are transmitted to the solution's users through the instant messaging system called Telegram.

Developed nations are undergoing a profound demographic transformation, characterized by rapidly aging populations and declining birth rates. This dual trend places unprecedented strain on healthcare systems, economies, and social support structures, creating complex biological, economic, and social challenges. This paper argues that current, often siloed, policy responses, such as pronatalist initiatives that overlook the equally urgent needs of older adults, are inadequate for addressing these interconnected issues. We propose that a comprehensive, transdisciplinary framework is essential for developing sustainable and ethical solutions. Through a review of demographic drivers, policy responses, and technological advancements, we analyze the limitations of fragmented approaches and explore the potential of innovative interventions. Specifically, we examine the role of artificial intelligence (AI) and robotics in transforming geriatric care. While these technologies offer powerful tools for personalizing treatment, enhancing diagnostics, and enabling remote monitoring, their integration presents significant challenges. These include ethical concerns regarding data privacy and comp

Voice-controlled interfaces can support older adults in clinical contexts -- with chatbots being a prime example -- but reliable Automatic Speech Recognition (ASR) for underrepresented groups remains a bottleneck. This study evaluates state-of-the-art ASR models on language use of older Dutch adults, who interacted with the Welzijn.AI chatbot designed for geriatric contexts. We benchmark generic multilingual ASR models, and models fine-tuned for Dutch spoken by older adults, while also considering processing speed. Our results show that generic multilingual models outperform fine-tuned models, which suggests recent ASR models can generalise well out of the box to real-world datasets. Moreover, our results indicate that truncating generic models is helpful in balancing the accuracy-speed trade-off. Nonetheless, we also find inputs which cause a high word error rate and place them in context.

Physical therapy (PT) is crucial in helping older adults manage chronic conditions and weakening muscles, but older adults face increasing challenges that can impact their PT experience, including increased fatigue, memory loss, and mobility and travel constraints. While current technology attempts to facilitate remote care, they have limitations and are used in-practice infrequently. Mixed reality (MR) technology shows promise for addressing these challenges by creating immersive, context-aware environments remotely that previously could only be achieved in clinical settings. To bridge the gap between MR's potential and its practical application in geriatric PT, we conducted in-depth interviews with three PT clinicians and six older adult patients to understand challenges with PT care and adherence that MR may address. Our findings inform design considerations for supporting older adults' needs through MR and outline technical requirements for practical implementation.

Building and deploying machine learning solutions in healthcare remains expensive and labor-intensive due to fragmented preprocessing workflows, model compatibility issues, and stringent data privacy constraints. In this work, we introduce an Agentic AI framework that automates the entire clinical data pipeline, from ingestion to inference, through a system of modular, task-specific agents. These agents handle both structured and unstructured data, enabling automatic feature selection, model selection, and preprocessing recommendation without manual intervention. We evaluate the system on publicly available datasets from geriatrics, palliative care, and colonoscopy imaging. For example, in the case of structured data (anxiety data) and unstructured data (colonoscopy polyps data), the pipeline begins with file-type detection by the Ingestion Identifier Agent, followed by the Data Anonymizer Agent ensuring privacy compliance, where we first identify the data type and then anonymize it. The Feature Extraction Agent identifies features using an embedding-based approach for tabular data, extracting all column names, and a multi-stage MedGemma-based approach for image data, which infers

Mental disorders including depression, anxiety, and other neurological disorders pose a significant global challenge, particularly among individuals exhibiting social avoidance tendencies. This study proposes a hybrid approach by leveraging smartphone sensor data measuring daily physical activities and analyzing their social media (Twitter) interactions for evaluating an individual's depression level. Using CNN-based deep learning models and Naive Bayes classification, we identify human physical activities accurately and also classify the user sentiments. A total of 33 participants were recruited for data acquisition, and nine relevant features were extracted from the physical activities and analyzed with their weekly depression scores, evaluated using the Geriatric Depression Scale (GDS) questionnaire. Of the nine features, six are derived from physical activities, achieving an activity recognition accuracy of 95%, while three features stem from sentiment analysis of Twitter activities, yielding a sentiment analysis accuracy of 95.6%. Notably, several physical activity features exhibited significant correlations with the severity of depression symptoms. For classifying the depress

The release of SAM 3D Body is a recent development in human mesh recovery, demonstrating improved performance in producing clean, topologically coherent meshes from single images. By leveraging the Momentum Human Rig (MHR), it achieves robustness to occlusion and diverse poses. However, our evaluation reveals a specific and consistent limitation: the model struggles to reconstruct detailed anthropometric deviations, particularly in populations exhibiting distinctive morphological alterations such as geriatric muscle atrophy, scoliosis, or pregnancy, even when these features are prominent in the input image. In this paper, we investigate this phenomenon not as a failure of the model's capacity, but as a byproduct of the "perception-distortion trade-off". We posit that the architectural reliance on the low-dimensional parametric MHR representation, combined with semantic-invariant conditioning (DINOv3) and annotation-based alignment, creates a pervasive "regression to the mean" effect. We analyze these mechanisms to understand why individual biological details are smoothed out. Furthermore, we state our contributions by proposing specific, constructive pathways for future work, such