Real-Time Frailty Detection for Elderly Care in Indoor Environments

Abstract

The rapid growth of the global older population has become a major public health concern, placing increasing pressure on healthcare systems to address age-related health condition effectively. Frailty syndrome is an age-related condition characterized by increased vulnerability due to reduced physiological reserves, leading to impaired gait, balance, and muscle weakness. It also causes functional decline, which elevates the risk of falls, disability, hospitalization, and rapid progression to dependency. Early identification of frailty is crucial for enabling timely preventive interventions and improving health outcomes and quality of life among elderly individuals. To address this challenge, this study presents a machine learning-based frailty detection system that enables continuous and remote monitoring of gait characteristics in indoor environments. The proposed system is integrated with an Ultra-Wideband (UWB) indoor positioning system to provide precise localization. It employs a wearable UWB tag equipped with a tri-axial accelerometer for data collection. Data stream processing techniques are applied to perform real-time gait analysis and frailty detection from acceleration data patterns. Experimental results demonstrate that the proposed system is practical for real-world deployment. Among the evaluated models, the Random Forest model achieved the best overall predictive performance, with an overall classification accuracy of approximately 87.32% and an average false positive rate of 6.3%.