Machine Learning in Ambient Assisted Living for Enhanced Elderly Healthcare: A Systematic Literature Review

As the global population ages, Ambient Assisted Living (AAL) systems have become essential in supporting the elderly to maintain independence and quality of life. Such systems integrate advanced technologies such as machine learning (ML), internet of things (IoT), and sensors to enhance safety and healthcare delivery. However, deploying such technologies raises significant challenges, especially in managing privacy, ensuring ethical compliance, and gaining user acceptance. This systematic literature review (SLR) explores the current state and advancements in AAL technologies, with a specific focus on their applications in elderly care. It synthesizes current methodologies, including predictive analytics, Explainable AI (XAI), and Generative AI (GenAI), while also evaluating the role of vision-based systems and multi-modal data fusion. It examines how such technologies are implemented to improve lives while also highlighting critical areas requiring attention, particularly privacy and ethical considerations. The review methodically analyzes articles and papers from the year 2020, selected based on their relevance to AAL technologies, their use of ML algorithms, and their focus on elderly care. The findings reveal the need for interpretability in AI-driven decisions and the role of GenAI in synthetic data generation and personalized conversational assistants. While ML and IoT significantly enhance AAL systems through predictive healthcare and personalized interventions, they also pose substantial privacy risks. The review identifies privacy as a critical concern due to the sensitive nature of the data collected and the vulnerabilities inherent in digital systems. Issues around data privacy, security breaches, and the need for robust privacy-preserving mechanisms are recurrent themes. This SLR illustrates the potential of AAL systems in elderly care and emphasizes the crucial requirement of addressing privacy and ethical issues to ensure such technologies are both beneficial and secure. The findings serve as a foundation for understanding the current state of AAL technologies and guide future advancements in elderly healthcare. © 2013 IEEE.