Machine Learning in Ambient Assisted Living for Enhanced Elderly Healthcare

The integration of Machine Learning (ML) within Ambient Assisted Living (AAL) frameworks is revolutionizing elderly healthcare by enabling proactive, intelligent, and explainable support systems. This study utilizes a vital-sign monitoring dataset comprising real-time physiological parameters—Heart Rate, Blood Pressure, Glucose Levels, and SpO₂—paired with threshold-based alerts and caregiver notifications. Such data mirrors the core of AAL systems, which depend on IoT-based continuous monitoring to detect health anomalies. Our analysis reveals that alerts are typically triggered when one or more vitals exceed safe limits, prompting timely caregiver intervention. ML models are applied to predict emergency patterns, reducing response time and enhancing elder safety. Further, Explainable AI (XAI) methods are incorporated to elucidate decision-making, thereby increasing trust among users and caregivers—for instance, highlighting exact parameter breaches that led to alerts. Additionally, multimodal data fusion integrates diverse sensor inputs, ensuring accurate and context-aware health assessments. Generative AI (GenAI) is also considered for augmenting data diversity and powering personalized virtual assistants. Despite technological advancements, this work emphasizes the importance of ethical considerations and privacy preservation, given the sensitive nature of health data. Secure architectures, encryption, and consent-driven data governance are recommended. Ultimately, this study demonstrates that ML-enhanced AAL systems can deliver timely, explainable, and secure health monitoring solutions that support aging in place with autonomy and dignity.

Keywords: Ambient Assisted Living, Elderly Healthcare, Machine Learning, Real-time Monitoring, IoT, Explainable AI, Vital Signs, Generative AI, Privacy, Caregiver Alert System.