| dc.description.abstract | The increasing elderly population, particularly those living alone, faces significant
challenges related to health monitoring and emergency response. Existing technologies
often lack personalization and generate false alerts, hindering their effectiveness in
ensuring the safety and well-being of elderly individuals. This review explores and
analyzes various technologies employed for monitoring behavioral patterns in elderly
individuals, focusing on smartwatch-based systems, machine learning integration,
and indoor localization techniques. A systematic examination of the literature was
conducted, highlighting the strengths and limitations of existing solutions. It was found
that while smartwatch-based systems demonstrate promising capabilities in detecting
falls and tracking health metrics, they frequently struggle with false alerts and limited
contextual integration. Machine learning algorithms, although highly accurate in
identifying behavioral anomalies, often rely on manually labeled data, restricting their
adaptability. Furthermore, indoor localization technologies present privacy challenges
that impact user acceptance. To bridge personalization and safety, this review clusters
its analysis into technology-wise, software-wise, and instrumental-wise categories. The
review emphasizes the need for more accurate and reliable solutions, calling for
advancements in personalization, real-time contextual awareness, and enhanced privacy
measures. Key findings suggest that integrating advanced AI techniques and secure
data handling processes will be crucial for the future development of elderly monitoring
systems | en_US |
