Recent Advances in Stretchable and Wearable Capacitive Electrophysiological Sensors for Long-Term Health Monitoring

Abstract

Over the past several years, wearable electrophysiological sensors with stretchability have received significant research attention because of their capability to continuously monitor electrophysiological signals from the human body with minimal body motion artifacts, long-term tracking, and comfort for real-time health monitoring. Among the four different sensors, i.e., piezoresistive, piezoelectric, iontronic, and capacitive, capacitive sensors are the most advantageous owing to their reusability, high durability, device sterilization ability, and minimum leakage currents between the electrode and the body to reduce the health risk arising from any short circuit. This review focuses on the development of wearable, flexible capacitive sensors for monitoring electrophysiological conditions, including the electrode materials and configuration, the sensing mechanisms, and the fabrication strategies. In addition, several design strategies of flexible/stretchable electrodes, body-to-electrode signal transduction, and measurements have been critically evaluated. We have also highlighted the gaps and opportunities needed for enhancing the suitability and practical applicability of wearable capacitive sensors. Finally, the potential applications, research challenges, and future research directions on stretchable and wearable capacitive sensors are outlined in this review.

Functional Two-Dimensional Materials for Bioelectronic Neural Interfacing

“…Typically, 2D rGO nanostructures are developed or grown during the fabrication process for neural interfaces at the heterointerfaces between the sensing components, which in turn facilitate swift charge transfer and low contact impedance at the neural interfaces. Non-invasive graphene-based polymer wearable sensors are well-developed for the characterization and precision sensing of biosignals with high levels of accuracy [ 20 , 21 , 22 ]. One of the main and widely available polymer substrates for fabrication of non-invasive thin-film wearable electronic neural interfaces is polydimethylsiloxane (PDMS).…”

“…The non-invasive recording of neural signals imposes a low level of health risk to users [ 15 , 16 ]. For instance, external flexible EEG neural interfaces enable the low-risking record of neural signals through wearable sensors for the monitoring of brain health activities and early diagnosis of neurodegenerative diseases [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. The non-invasive neural interfacing by biocompatible, wearable electrodes is employed along with other clinical brain signal monitoring and medical monitoring systems such as magnetic resonance imaging (MRI), computed tomography (CT) scans and intracranial electroencephalography (iEEG) [ 25 , 26 ].…”

Biphasic liquid metal mixtures in stretchable and flexible applications

“…Flexible and stretchable electronics constitute a novel technology with potential applications in fields such as monitoring, sensing, and medical devices. 1,2 In wearable and soft electronic devices, various flexible soft polymers, such as polydimethylsiloxane (PDMS), Ecoflex, silicone, polyurethane, polyimide, parylene, and polymethyl methacrylate, have been proposed as substrate materials, [3][4][5] exhibiting durability and biocompatibility. Conductive materials, however, must be improved for electrodes.…”

An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal

“…This trend of wearable devices imposes the need for developing data-driven and true generic device-independent (patient-specific) methods for ECG signal analysis. Recently flexible and stretchable wearable sensors play a great role in this regard with the emerging of different flexible materials as well as designing strategies of sensors [45]. Implementing such a strategy, numerous efforts, for example [46] have been observed to classify the abnormality of ECG beats using different methods.…”