Development of machine learning and tailored image compression algorithms in beehive monitoring system

Abstract

Stroke is a significant global health concern with devastating consequences for individuals and their families. Early detection plays a crucial role in preventing or minimizing the impact of strokes. This research presents a novel approach using low-cost IoT technology to develop a wearable device capable of predicting stroke onset in high-risk patients. The findings of this study have the potential to revolutionize stroke prevention and improve patient outcomes. The main problem addressed in this work is the challenge of timely stroke detection, which is critical for effective intervention. Existing methods for stroke prediction often require expensive equipment and specialized settings, limiting their accessibility and scalability. The scope of this project is to develop a low-cost wearable device that utilizes photoplethysmography (PPG) to measure pulse pressure and mean arterial pressure, key indicators of stroke risk. The main argument is that this device can provide an affordable and convenient solution for early stroke prediction. The research methodology involved collecting physiological data using an Arduino Nano RP2040 and a MAX30102 sensor. The device utilized PPG to measure pulse pressure and mean arterial pressure. The collected data was wirelessly transmitted via Wi-Fi and delivered to a designated email address using the IFTTT key. This allowed for further analysis and monitoring of the recorded parameters. The successful development of a low-cost IoT-based wearable device for stroke prediction has demonstrated its potential in assessing stroke risk. The device accurately measures pulse pressure and mean arterial pressure, enabling remote data transfer and continuous monitoring. This research contributes to stroke prevention by providing an affordable and accessible solution for early stroke prediction. Further validation and collaboration with healthcare experts are needed to enhance accuracy and reliability.