Intelligent pressure and temperature sensor algorithm for diabetic patient monitoring: An IoT approach

Abstract

The present study evaluated how an Internet of Things (IoT)-based innovative algorithm could employ feature values to identify distinct plantar foot locations. The proposed system could also assess static and dynamic plantar pressure conditions through an enhanced feature extraction method. This study emphasized the significance of intelligent systems in monitoring diabetic patients and their potential to improve patients' lives. The proposed IoT-centred approach offers a promising solution for accurately determining unique foot locations and plantar pressure parameters. The algorithm could predict potential diabetic issues in advance via an optimized feature extraction, aiding proactive interventions. Available systems need to be improved to provide real-time data. Furthermore, fundamental alerts might be a nuisance for the users. Consequently, this study proposes a more personalized and context-aware monitoring device. The findings provided insights into innovative sensor employment in diabetic patient care and underscored IoT's role in refining the system's accuracy and reliability.