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.