Emerging Trends in Signal Processing for Wearable Electronics: A Review

Abstract

Wearable electronics have undergone a substantial evolution in recent years, transitioning from basic activity trackers to sophisticated devices capable of real-time monitoring of diverse physiological parameters. This advancement has been made possible by imposing signal processing techniques to extract actionable insights from the raw sensor data collected by these wearables. This review article delves into the emerging trends in signal processing methods specifically tailored for wearable electronics. By analyzing recent developments, we aim to provide a comprehensive understanding of the state-of-the-art techniques and their applications in this rapidly evolving field. We begin by exploring the pivotal role of signal processing in transforming raw sensor data into meaningful information. This includes techniques such as feature extraction, noise reduction, pattern recognition, and data fusion. These methods enable wearables to provide valuable insights into various aspects of human health and behaviour, ranging from activity levels and sleep patterns to cardiovascular health and stress levels. Despite the significant progress achieved, several challenges persist in the realm of signal processing for wearable electronics. These challenges encompass power consumption constraints, limited computational resources, and the critical need for real-time processing capabilities. Furthermore, ensuring data privacy and security remains a paramount concern, particularly when dealing with sensitive health information.