Research Publications
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Item Embargo AI-Driven Integrated Caregiving and Health Monitoring Framework for Elderly Well-Being(Institute of Electrical and Electronics Engineers, 2026-07-22) Nugaliyadde, S; Nikeshi, N; Marasinghe, M; Rajapaksha, C; Rajapaksha, S; Thelijjagoda, SThe rapid growth of the aging population has brought about some serious challenges, particularly in managing illnesses, feelings of loneliness, cognitive decline, and mental health issues. Traditional caregiving methods often depend on occasional assessments and hands-on supervision, which can fall short in providing the ongoing and adaptable support that’s really needed. This paper introduces an innovative caregiving and monitoring framework powered by AI, aimed at offering integrated, real-time, and comprehensive assistance for older adults. The system harnesses the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and health data analytics to combine physical health monitoring, nutrition planning, smart routine coaching, and therapist-led mental health support all in one platform. With features like voice-based conversations and journaling, it makes emotional expression and behavioral analysis more accessible, helping to gain a deeper insight into users’ mental well-being. Predictive analytics and anomaly detection are used to spot early signs of health risks and shifts in behavior, allowing for timely interventions. Plus, remote access means caregivers and healthcare professionals can keep an eye on users and offer informed advice. By shifting caregiving from a reactive approach to a proactive and preventive one, this system not only improves quality of life but also encourages independent living and eases the burden on caregivers.Item Embargo Sinhala Speech Recognition System for Speech-Based Autism Intervention in Children Using the NAO Robot(Institute of Electrical and Electronics Engineers Inc., 2026-03-26) Bopage, H; Pulasinghe, K; Rajapaksha, SThis research focuses on the development of a Sinhala speech recognition engine tailored to identify the language content of conversations with children. The engine leverages machine learning algorithms and natural language processing (NLP) techniques to transcribe and classify speech in Sinhala. Key features include an acoustic model optimized for the nuances of Sinhala phonetics and a language model trained on datasets encompassing texts of child-directed speech. The system evaluates linguistic aspects to assess the appropriateness of content and engagement levels in child-centric dialogues. By addressing challenges such as phoneme variation and informal conversational patterns, the system aims to enhance the understanding and facilitation of Sinhala-based child interactions, promoting effective communication and developmental support.