Browsing by Author "Samaraweera, M"

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    AI-Driven Behavioral Assessment and Intervention for ADHD
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dharmasena U.D.S.V; Manamperi R.S; Dilshani H.T.D.P; Halliyadda H.U.M.S.; Kasthuriarachchi, S; Samaraweera, M
    Attention Deficit Hyperactivity Disorder (ADHD) affects millions of children worldwide, causing cognitive, behavioral,and academic challenges. Traditional diagnostic methods rely on subjective assessments, leading to inconsistent evaluations and delayed interventions. This research addresses these limitations by developing an AI-driven gamified behavioral assessment and intervention platform that integrates machine learning, real-time emotion recognition, and adaptive learning techniques. The system features interactive games that dynamically adjust difficulty based on behavioral and emotional responses captured through facial expression analysis. Data collection includes gameplay metrics, parent-reported questionnaires, and expert evaluations to train AI models for ADHD classification and personalized intervention. The emotion recognition module achieved 89% accuracy using Convolutional Neural Networks, while reinforcement learning algorithms enabled real-time game adaptation. Classification models, including Random Forest (91.3% accuracy), demonstrated strong predictive capabilities. The platform provides continuous monitoring dashboards for caregivers and educators, enabling data-driven decision-making. Results indicate that AI-based behavioral assessments offer improved accuracy and flexibility compared to traditional methods, with emotion-adaptive gaming enhancing engagement. This research demonstrates the potential of AI-powered solutions to transform ADHD diagnosis and intervention through improved efficiency, personalization, and accessibility.
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    PublicationOpen Access
    Gesture driven smart home solution for bedridden people
    (Association for Computing Machinery, 2020-09-21) Jayaweera, N; Gamage, B; Samaraweera, M; Liyanage, S; Lokuliyana, S; Kuruppu, T
    Conversion of ordinary houses into smart homes has been a rising trend for past years. Smart house development is based on the enhancement of the quality of the daily activities of normal people. But many smart homes have not been designed in a way that is user friendly for differently-abled people such as immobile, bedridden (disabled people with at least one hand movable). Due to negligence and forgetfulness, there are cases where the electrical devices are left switched on, regardless of any necessity. It is one of the most occurred examples of domestic energy wastage. To overcome those challenges, this research represents the improved smart home design: MobiGO that uses cameras to capture gestures, smart sockets to deliver gesture-driven outputs to home appliances, etc. The camera captures the gestures done by the user and the system processes those images through advanced gesture recognition and image processing technologies. The commands relevant to the gesture are sent to the specific appliance through a specific IoT device attached to them. The basic literature survey content, which contains technical words, is analyzed using Deep Learning, Convolutional Neural Network (CNN), Image Processing, Gesture recognition, smart homes, IoT. Finally, the authors conclude that the MobiGO solution proposes a smart home system that is safer and easier for people with disabilities