arXiv:2511.14620v2 Announce Type: replace 
Abstract: Falls are a leading cause of injury and loss of independence among older adults. Vision-based fall prediction systems offer a non-invasive solution to anticipate falls seconds before impact, but their development is hindered by the scarcity of available fall data. Contributing to these efforts, this study proposes the Biomechanical Spatio-Temporal Graph Convolutional Network (BioST-GCN), a dual-stream model that combines both pose and biomechanical information using a cross-attention fusion mechanism. Our model outperforms the vanilla ST-GCN baseline by 5.32% and 2.91% F1-score on the simulated MCF-UA stunt-actor and MUVIM datasets, respectively. The spatio-temporal attention mechanisms in the ST-GCN stream also provide interpretability by identifying critical joints and temporal phases. However, a critical simulation-reality gap persists. While our model achieves an 89.0% F1-score with full supervision on simulated data, zero-shot generalization to unseen subjects drops to 35.9%. This performance decline is likely due to biases in simulated data, such as 'intent-to-fall' cues. For older adults, particularly those with diabetes or frailty, this gap is exacerbated by their unique kinematic profiles. To address this, we propose personalization strategies and advocate for privacy-preserving data pipelines to enable real-world validation. Our findings underscore the urgent need to bridge the gap between simulated and real-world data to develop effective fall prediction systems for vulnerable elderly populations.

تقدم دراسة جديدة شبكة التحويل البيوميكانيكية الزمانية المكانية (BioST-GCN)، وهو نموذج ذو مسارين مصمم لتحسين التنبؤ بالسقوط لدى كبار السن من خلال دمج بيانات الوضعية والبيوميكانيكا. يُظهر هذا النموذج أداءً محسّنًا مقارنةً بالطرق الحالية، حيث يحقق درجات F1 ملحوظة على مجموعات بيانات محاكاة. ومع ذلك، لا تزال هناك تحديات في سد الفجوة بين المحاكاة والواقع، خاصة في التطبيقات الواقعية.

Un nuevo estudio presenta la Red de Convolución Gráfica Espacio-Temporal Biomecánica (BioST-GCN), un modelo de doble flujo diseñado para mejorar la predicción de caídas en adultos mayores al integrar datos de postura y biomecánicos. Este modelo demuestra un rendimiento mejorado en comparación con los métodos existentes, alcanzando puntuaciones F1 notables en conjuntos de datos simulados. Sin embargo, persisten desafíos para cerrar la brecha entre la simulación y la realidad, especialmente en aplicaciones del mundo real.

Une nouvelle étude présente le Réseau de Convolution Graphique Spatio-Temporel Biomecanique (BioST-GCN), un modèle à double flux conçu pour améliorer la prédiction des chutes chez les personnes âgées en intégrant des données de pose et biomécaniques. Ce modèle montre des performances améliorées par rapport aux méthodes existantes, atteignant des scores F1 notables sur des ensembles de données simulées. Cependant, des défis subsistent pour combler l'écart entre la simulation et la réalité, en particulier dans les applications réelles.

A new study introduces the Biomechanical Spatio-Temporal Graph Convolutional Network (BioST-GCN), a dual-stream model designed to enhance fall prediction for older adults by integrating pose and biomechanical data. This model demonstrates improved performance over existing methods, achieving notable F1-scores on simulated datasets. However, challenges remain in bridging the simulation-to-reality gap, particularly in real-world applications.

Fusing Biomechanical and Spatio-Temporal Features for Fall Prediction: Characterizing and Mitigating the Simulation-to-Reality Gap

arXiv:2511.18334v1 Announce Type: new 
Abstract: Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions ("I don't know") when the ML model's confidence is low. Evaluated on real-world data from eight smart homes, our method outperforms baseline methods in recall and other classification metrics while maintaining the lowest abstention proportion and interval width. A survey of 42 nurses confirms that our system's outputs are valuable for guiding clinical decision-making, underscoring their practical utility in improving informed decisions and effectively managing UTIs and other condition flare-ups in older adults.

تم تطوير نظام جديد للمنزل الذكي مع طبيب في الحلقة للكشف عن تفجرات عدوى المسالك البولية (UTI) لدى كبار السن، باستخدام بيانات المستشعرات المحيطة وتقنيات التعلم الآلي. يهدف هذا النهج المبتكر إلى توفير الكشف المبكر عن عدوى المسالك البولية، والتي غالبًا ما يتم تجاهلها حتى تصبح مشاكل صحية خطيرة.

Se ha desarrollado un nuevo sistema de hogar inteligente con un clínico en el bucle para detectar brotes de infecciones del tracto urinario (ITU) en adultos mayores, utilizando datos de sensores ambientales y técnicas de aprendizaje automático. Este enfoque innovador tiene como objetivo proporcionar una detección temprana de las ITU, que a menudo se pasan por alto hasta que se convierten en problemas de salud graves.

Un nouveau système intelligent de maison avec un clinicien dans la boucle a été développé pour détecter les poussées d'infections urinaires (IU) chez les personnes âgées, utilisant des données de capteurs ambiants et des techniques d'apprentissage automatique. Cette approche innovante vise à fournir une détection précoce des IU, souvent négligées jusqu'à ce qu'elles deviennent graves.

A new clinician-in-the-loop smart home system has been developed to detect urinary tract infection (UTI) flare-ups in older adults, utilizing ambient sensor data and machine learning techniques. This innovative approach aims to provide early detection of UTIs, which are often overlooked until they escalate into severe health issues.

Fusing Biomechanical and Spatio-Temporal Features for Fall Prediction: Characterizing and Mitigating the Simulation-to-Reality Gap

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