Review: Developments and challenges of advanced flexible electronic materials for medical monitoring applications

Flexible sensors, made from flexible electronic materials, are of great importance in the medical field due to the rising prevalence of cardiovascular and cerebrovascular diseases. Studies have demonstrated that timely diagnosis and continuous monitoring of relevant physiological signals can be beneficial in preventing such conditions. Although traditional rigid monitoring sensors are still widely used for medical monitoring, the EMG, ECG, and EEG signals they obtain are often significantly affected by motion artifacts and noise. Therefore, the significance of wearable smart monitoring devices based on flexible electronic materials cannot be overstated. Numerous researchers have been working tirelessly for this purpose, exploring solutions from various angles, including material choice, circuit design, and algorithmic processing. This paper begins by analyzing the causes of motion artifacts in medical smart monitoring devices. Next, it introduces the application of flexible materials and flexible electronic materials in several aspects, along with the work of some representative flexible sensors. Following this, it discusses materials selection and device designs (e.g., accelerometers, gyroscopes, differential circuits, etc.) and algorithmic approaches for eliminating motion artifacts. Finally, an outlook on motion artifact removal techniques from the perspectives of more in-depth material development, structural design, and machine learning is provided. The purpose of this paper is to offer a comprehensive overview of current motion artifact removal techniques and materials, aiming to encourage further research and effectively address the key problem of signal acquisition accuracy in smart biomonitoring.

Graphical Abstract

TOC: Motion artifact occurrence state [40, 120]. Two methods of motion artifact removal or attenuation states are now commonly used: device design [30, 109] and algorithm development [115]. Future development focusing on machine learning and AI. [136]