Browsing by Author "Gunarathna, T. G. L."

Now showing 1 - 3 of 3

A cost-effective and adaptable queue management system to increase efficiency in patient queue management
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Adhikari, A. M. N. D. S.; Gunarathna, T. G. L.; Bandara, K. D. Y.; Gunawardana, K. D. B. H.; Seneviratne, J. A.; Perera, M. H. M. T. S.
Healthcare systems worldwide, particularly in resource-limited settings like Sri Lanka, face significant challenges related to high patient volumes and constrained resources. These challenges often lead to extended wait times and reduced patient satisfaction. This study presents an innovative, adaptable queue management system designed to replace inefficient manual methods, enhance operational efficiency, and optimise patient flow. Scalable to meet the needs of both small clinics and large hospitals, the system functions across various connectivity scenarios, ensuring flexibility in diverse environments. The system comprises patient, doctor, and administrative interfaces. Upon patient registration, a QR code will be generated, and the patient can use the QR code to check-in. A printed queue token will be issued when a patient checks-in. Doctors can manage their queues and access real-time patient information. Administrators oversee overall system operations, including advertisement management and key performance indicator (KPI) tracking, to monitor and enhance healthcare delivery in addition to having the ability to add, remove, or edit users. Built on a robust technology stack that includes HTML, CSS, JavaScript, PHP, SQLite3 for database management, and AES-256-CBC encryption for secure data handling, the system is designed for reliability and scalability. Embedded ESP32 devices with OLED displays and LEDs provide offline functionality, while multicast DNS (mDNS) ensures seamless device connectivity to local networks without requiring Internet access which is critical for rural healthcare facilities. The system features a custom-built algorithm, leveraging Random Forest Regression, to analyse historical and real-time queue data. This allows for precise queue time estimates and significantly improves staff and patient planning. The system outperforms the traditional manual systems, which lack both real-time prediction capabilities and efficiency. The system performance was meticulously improved using various optimisation techniques such as batch processing, database indexing, and algorithm optimisation, which led to an execution time of 22 seconds to be brought down to 1.5 seconds on a 1.4 million row data set, where the execution involved processing, sorting, encrypting, decrypting, and storing data. A one-tailed t-test was performed to compare the execution times of test runs with optimisation and without optimisation. There was a significant difference in execution times between test runs without optimization (M = 21.84, SD = 1.16) and execution times between test runs with optimization (M = 1.52, SD = 0.28); t(43) = 107.76, p < 0.001. The system was validated for 10 years of sample data and the results demonstrate that the system is robust and responsive under real-world conditions. Continuous validation is ongoing in diverse healthcare environments to further assess its impact on optimizing queue management, resource allocation, and patient satisfaction. This scalable and adaptable system represents a substantial advancement in healthcare management, offering a transformative solution to meet the evolving needs of healthcare facilities despite scarce infrastructure.
Harvesting energy from human-body movements for ultra-low power appliances
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Gunarathna, T. G. L.; Rupasingha, U. S. D. B. M.; Gunasekara, H. S.; Thennakoon, S. E. R. T. M. M. I.; Senanayake, S. V.; Leanage, H. B.; Kumarage, W. G. C.; Ranaweera, A. L. A. K.
Energy harvesting from human body movements presents a promising approach to sustainably power wearable devices and sensor nodes. This study explores the potential of capturing energy from footsteps using piezoelectric technology. A critical aspect of this technology involves designing an efficient interface between the piezoelectric elements and the electrical load to maximize energy conversion. The irregular and low-frequency nature of human footsteps poses a significant challenge, resulting in low energy extraction. Moreover, achieving a self-powered circuit adds another layer of complexity. To address these challenges, a novel Parallel-Synchronous Switching Harvesting on Inductor (P-SSHI) circuit is proposed. This circuit increases the energy extraction efficiency of piezoelectric elements. Since the output of a piezoelectric element is in the form of alternating current (AC), a MOSFET-based full-bridge rectifier circuit is proposed to convert AC to direct current (DC). As proof of concept, a shoe insole integrated with multiple piezoelectric elements connected in parallel was developed, and the energy conversion circuit was rigorously validated. The system was tested at a frequency of 1 Hz, which corresponds to the typical walking frequency, using a person weighing 60 kg. Under these conditions, the proposed system achieved an average power output of 550 µW per step with a 10 kΩ resistive load and a 10 µF storage capacitor. The effectiveness of the system was further validated by demonstrating its ability to charge a 1 mF capacitor to 2.1 V in 18 steps and a 10 µF capacitor to 7.0 V in a single step. Notably, the circuit is self-powered and capable of initiating operation without the assistance of an external battery, highlighting its potential for autonomous use. The circuit was prototyped using simple discrete components, emphasizing its practicality and feasibility for real-world applications. The proposed MOSFET-based rectifier circuit offers a significant advantage in converting AC to DC with minimal voltage drop, compared to conventional diode full-bridge rectifiers. Furthermore, the system's capability to charge a Li-ion battery (3.7 V, 300 mAh) was demonstrated, showcasing the potential of the wearable piezoelectric energy harvesting system to provide a sustainable power supply for wearable wireless sensors. Future studies will focus on optimizing energy harvesting under different walking conditions, integrating energy storage devices, and enhancing durability. The proposed technology also shows promise for applications in diverse fields such as healthcare, fitness monitoring, and environmental sensing, where reliable, self-sustaining wearable power solutions are in high demand.
Wireless pager system for enhancing emergency communication in hospital environment
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Gunarathna, T. G. L.; Adhikari, A. M. N. D. S.; Bandara, K. D. Y.; Gunawardana, K. D. B. H.; Seneviratne, J. A.; Perera, M. H. M. T. S.
Maintaining fast and efficient communication between hospital staff is critical to ensure patient safety during emergencies. However, challenges such as the lack of Global System for Mobile Communications (GSM) signals in countries like Sri Lanka and the risk of using cable communication during hazardous weather conditions further complicate emergency communication. This paper proposes a wireless pager system utilizing LoRa (Long Range) technology to facilitate seamless interaction between doctors, nurses, and other supportive and administrative staff in a hospital. LoRa operates on sub-gigahertz frequencies, providing robust signal penetration and extended range, making it ideal for hospital environments where walls and infrastructure often disrupt traditional signals. The proposed system consists of three primary modules: the Ward Module, Central Hub, and Doctor Module. The Ward Module, placed in hospital wards, allows nurses to trigger emergency alerts by selecting an available doctor. It also provides status updates on message delivery and doctors' responses. The Central Hub acts as the system's control center, maintaining a database of doctors and wards, managing doctor availability, registering new entries, and logging communication transactions. It utilizes a web-based application to handle and collect data, which runs on the Central Hub, streamlining data management and access. The Hub also backs up data to the cloud and stores it locally during internet outages, synchronizing once the connection is restored. The Doctor Modules enable doctors to log their presence by selecting their ID from a list obtained from the Central Hub. This login data is updated in the Central Hub and shared with the Ward Modules. Upon receiving an emergency alert, doctors can respond by accepting, canceling, or forwarding the message, with the updated status being communicated back to the Ward Module. The system was tested in a simulated hospital environment using two Ward Modules, two Doctor Modules, and a Central Hub, covering a 200m distance. Both the Ward and Doctor Modules were built using ESP32 microcontrollers with LoRa modules operating at 433 MHz, while the Central Hub was developed using a Raspberry Pi single board computer with a LoRa module. The system demonstrated reliable performance, maintaining stable communication across the test range. It also demonstrated potential for larger hospitals, with extended range possible through proper antenna configuration. A 96% success rate was recorded, with message transmission in under 2 seconds. While LoRa offers robust long-range communication with low power use, its limited bandwidth poses challenges for large data transmission. However, for emergency pager systems, the trade-off between power efficiency and data capacity is acceptable. The system operates independently of traditional communication infrastructure, providing hospitals with a sustainable and resilient solution for emergency communication. It streamlines emergency response in hospital wards by enabling realtime communication and status updates between staff, ensuring fast and accurate transmission of critical information. This enhances the efficiency of interventions and improves patient care outcomes.