Development of a Firmware for a Wearable Embedded System based on WI-FI for the Monitoring of Neurological Pathologies

Neurological pathologies frequently require continuous and reliable monitoring of patient movements, especially during rehabilitation. This thesis will follow the steps of the development of a firmware for a wearable embedded system that uses Wi-Fi networking to send the motion data that has to be analysed. The Zephyr Real-Time Operating System (RTOS), which was selected for the system due to its robust Internet of Things (IoT) capabilities and modular architecture, serves as its foundation.A low power 9-axis inertial sensor was selected for gathering movements data. The firmware efficiently manages data acquisition, processing, and wireless transmission to external devices or cloud platforms for further analysis. The Zephyr RTOS ensures real-time performance and energy efficiency, essential for wearable applications. The firmware’s functionality includes reading data from the inertial sensors, noise reduction, and the provision of a message broker for bidirectional data transfer. Data validation is performed by comparing computed results with values provided by the senso’s integrated Digital Motion Processor. By improving remote monitoring of patients undergoing rehabilitation, the developed firmware aims to provide to clinicians a new instrument to gather significant data. The integration of real-time data processing, embedded systems, and IoT creates new opportunities to enhance the treatment of neurological conditions.