Browsing by Author "Swarnakantha, N.H.P.R.S"

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    An integrated data-driven approach for Chronic Kidney Disease of Unknown Etiology (CKDu) risk profiling and prediction in Sri Lanka
    (SPIE, 2025) Rajapaksha, N; Rajawasan, H; Ubeysinghe, R; Perera,S; Swarnakantha, N.H.P.R.S; Gamage, M; Nanayakkara, N; Wijayakulasooriya, J; Herath, D; Lakmali, M
    Chronic kidney disease of unknown etiology is a significant public health issue in Sri Lanka, especially in rural farming communities. The exact causes remain unclear, with potential links to environmental and socio-economic factors. This research employs Biological Data and Geographic Information Systems to analyze risk factors such as water quality, agricultural practices, climatic conditions, Demographic Factors, Socio-economic Factors. This study uses data from government health records, the Centre for Research-National Hospital Kandy, and field surveys. By identifying patterns and correlations, the study aims to inform public health interventions and reduce the impact of CKDu, ultimately improving health outcomes for affected populations. This will greatly contribute to preventing the disease, reducing the risk, and identifying patients at an early stage.
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    PublicationEmbargo
    IoT Based Smart Pillow for Improved Sleep Experience
    (IEEE, 2022-12-09) Ravishani, W.M.S; Ganepola, G.A.S; .Silva, E.D.M; Chamodi Jayanika, G.H.G.; Rajapaksha, U.U.S; Swarnakantha, N.H.P.R.S
    Maintaining appropriate health by avoiding illnesses brought on by stress, heart disease, stroke, insomnia, and hormonal imbalance is made possible by managing the quality of sleep necessary for brain and memory-related tasks. In order to reduce these phenomena, we concentrated on recognizing them and developing strategies to do so. As a result, we decided to use smart pillows and bands that are Internet of Things (IoT)-based. To connect the touch sensor and relay module for improving sleep quality with the help of an automatic alarm system and light treatment system, an ESP-32 (microcontroller) was built into the pillow. The band will also have a second ESP 32 that can be connected to an oximeter, gyro, and accelerometer to improve the sleepwalk alert and health monitoring systems’ accuracy. The mobile application will also be created so that the patient and the doctor may review the patient’s sleeping patterns, and the CNN-based deep learning architecture was used to develop the emotion recognition function that uses music to improve sleep quality. For a better sleep experience, we will refer to the smart band and pillow as ”MAGICAL PILLOW” and ”MAGICAL BAND” as the ultimate products.