Faculty of Computing

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4776

Browse

Filters

2025 - 20262

Advanced Search

Filter by

Settings

Start date: 2025Subject: search.filters.subject.emotion recognition

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemEmbargo
    Federated Edge Intelligence for Adaptive Health Prediction in Elderly Care
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-17) Silva G.D.T; Sineth H.M.G; Constantine S.A.D.N.C.H; Dissanayake M.E; Pandithage, D; Lokuliyana, S
    This research presents the development and evaluation of an IoT-based nursing home monitoring system designed to enhance elderly healthcare through real-time detection, predictive alerts, and caregiver support. The system integrates multimodal sensing with edge and server-level machine learning to monitor three critical areas: sleep quality, fall detection, and emotion recognition in dementia patients. Hardware components, including a Raspberry Pi and non-intrusive sensors, were combined with lightweight neural networks to ensure low-latency processing. Detailed model optimizations, including 8-bit quantized TensorFlow Lite MobileNetV2 and CNN-LSTM pipelines, enabled on-device inference within 80-120 ms. A caregiver dashboard and mobile application provided intuitive visualization of real-time alerts and long-term health reports. Benchmarking against threshold-based and classical ML baselines demonstrated 9-18% performance improvements and 56% lower alert latency. Experimental evaluation demonstrated promising outcomes, with fall detection achieving 95% accuracy, emotion detection 91%, and sleep monitoring 90% reliability. System uptime reached 98%, and alerts were delivered within two seconds in 96% of cases. These results indicate that the proposed solution can effectively reduce caregiver burden, improve resident safety, and establish a scalable framework for intelligent elderly healthcare monitoring.
  • Thumbnail Image
    ItemEmbargo
    AI-Powered Mobile Application for Supporting Mental Wellness in Children
    (Institute of Electrical and Electronics Engineers, 2026-06-26) Pathiraja G.P.K; Navarathne N.M.D.C; Navodya D.P.D.D.; Dharmapriya R.A.P.; Vidanaralage, A.J; Vidanaralage, A.J
    Children often struggle to express their emotions verbally, making early detection of mental health issues challenging and limiting timely intervention. This research proposes an AI-powered mobile application designed to support children's mental well-being through multimodal emotional analysis and personalized interventions. The significance of this study lies in addressing the lack of accessible, child-friendly, and culturally relevant digital mental health solutions, particularly for Sinhala-speaking users, while reducing dependence on subjective expert interpretation. The proposed system integrates four main components, including a culturally grounded therapeutic story generator using a GRU-based deep learning model, an emotion analysis module for children's drawings utilizing DenseNet121 and YOLOv8 with colour and spatial feature extraction, an emotion-aware music recommendation and adaptive puzzle game powered by EfficientNet-based facial emotion recognition, and a Sinhala voice-first mood prediction system using fine-tuned XLM-RoBERTa.Experimental results demonstrate strong performance, including improved contextual coherence and adaptive therapeutic storytelling capabilities, 79% accuracy in drawing-based emotion classification, 87% accuracy in emotion-aware music recommendation, and 96% accuracy in Sinhala text-based mood prediction, along with improved user engagement through personalized storytelling and adaptive feedback mechanisms. The system further enhances interpretability by generating parent-friendly emotional reports using large language models. In conclusion, this research contributes a comprehensive, multimodal, and culturally adaptive AI solution for supporting children's mental health, enabling early emotional detection, improved communication, and scalable intervention through an engaging mobile platform.