The 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.
Sayumi Nugaliyadde, Nawodya Nikeshi, M. Marasinghe et al.· 2026 4th International Confe...· 0 citations
This paper presents the Integrated Predictive Intelligence Tool for Pediatric Health (IPITPH), a mobile-enabled AI-driven decision support system for addressing complex pediatric healthcare challenges in resource-limited, multilingual settings. The system comprises four integrated modules: a hybrid LSTM-DNN predictive analytics module incorporating culturally specific dietary patterns and temporal growth signals for pediatric risk prediction, an LLM-based nutrition optimization module generating personalized meal plans aligned with clinical guidelines and caregiver behavior, a multimodal emergency response module for real-time triage and teleconsultation, and a Retrieval-Augmented Generation-based multilingual conversational AI enabling voice-first caregiver interaction in Sinhala, Tamil, and English. Evaluated on pediatric data from 69 Sri Lankan families supplemented by the PIC clinical database, the predictive module achieved 3.18% MAPE for height prediction and 0.975 AUC for multi-domain risk classification. The nutrition module demonstrated high caloric precision and behavioral adaptation, the emergency module achieved robust triage accuracy with sub-100 ms edge latency, and the conversational module showed significant improvements in clinical accuracy and WHO guideline alignment across all three languages. These results establishing IPITPH as a promising unified AI framework for community-based pediatric healthcare in resource-limited regional contexts, pending broader prospective validation.
Munasing A. R. Tharana, Tharindu E. Nawarathne, Githadi S. Wijayarathna et al.· 2026 IEEE International Conf...· 0 citations