Predicting Potential Indicators of Early Stages of Prostate Disease in Elderly Using Magnet Sensors

The aging population has led to an urgent need for innovative solutions to monitor and improve the quality of life for the elderly. This study explores the possibility of predicting the early stages of urinary diseases, particularly prostate disease, using a non-evasive approach using magnet sensor technology to monitor the elderly urination patterns. The study also addresses some of the issues related to maintenance and privacy found by previous researchers when trying to monitor activities of daily living to identify diseases. In this study, we used a prototype system based on magnets and magnetic sensors to collect the necessary data and later process and analyze it on a Raspberry Pi. This system solves the identified issues by leveraging the unique properties of magnetic sensors, such as their insensitivity to external interferences, almost unlimited service life, and high level of reliability. The prototype monitors four activities of daily living to help trace and monitor urination patterns to detect potential early signs of prostate disease.Furthermore, the prototype stores only binary data, ensuring privacy protection in case of a security breach. A comparative sensor technology evaluation with existing solutions that monitor and track events related to urinary activity patterns was conducted. As a result, our proposed technology appeared as the ideal candidate solution for our targeting objective, and we observed that our system successfully detected all expected events and activities related to urination patterns with high reliability and minimal maintenance. Future work will encompass implementing the system in real-life surroundings and continuously valuing its performance for improvements.