Remote Healthcare Monitoring for the Aging Population: A Systematic Review of Datasets, Techniques, and Challenges

The rapid growth of the aging population has increased the demand for innovative healthcare solutions, particularly in fall detection, vital sign monitoring, and activities of daily living. Older adults are more vulnerable to health concerns, and timely intervention through remote monitoring systems Can help mitigate the risks that come with sudden illnesses. and falls. The paper describes a systematic literature survey. (SLS) of remote monitoring technologies for elderly healthcare. focusing on the datasets, techniques, and methodologies published. During the period 2019-2025, the survey highlights the integration of deep learning models and telehealth technologies to provide continuous and real-time care to the elderly, supporting global health objectives. We analyze 15 publicly available datasets, examining their characteristics, sensor modalities and preprocessing techniques, and applicability to machine learning and deep learning. (DL) models. Main challenges include diversity of datasets, real-world Applicability, and relevant privacy concerns are discussed along with potential solutions such as multi-sensor fusion, hybrid models. and edge computing. The findings provide guidelines for researchers and practitioners, while providing a roadmap for future advances in Remote healthcare monitoring of the aging population.