Abstract

This paper presents the design and implementation of real-time embedded systems leveraging the Average Threshold Crossing (ATC) approach for IoT applications, with a focus on event-driven surface Electromyography (sEMG) data acquisition. The increasing demand for IoT devices in healthcare and wearable technology necessitates efficient and reliable real-time processing of physiological signals such as sEMG. However, existing approaches often face challenges in achieving the required speed and accuracy while maintaining low power consumption. This paper addresses this gap by proposing an ATC-based approach that enhances real-time processing capabilities while minimizing energy consumption. The methodology involves the development of specialized hardware and software components tailored to the requirements of event-driven sEMG IoT acquisition. Experimental results show the effectiveness of the proposed approach in achieving high accuracy and low latency in real-time sEMG data processing for IoT applications.