Decoding tissue biomechanics using conformable electronic devices

Abstract

Understanding the human body’s tissue biomechanics — the physical deformation and variations in intrinsic mechanical properties — has considerable potential in health monitoring, disease diagnosis and bioengineering. However, current tools for decoding tissue biomechanics rely on rigid and bulky devices that are not compatible with biological tissues. Such a discrepancy results in inaccurate measurement and even pain and discomfort for the subjects undergoing the measurement. To overcome the limitations of current tools, conformable electronic devices have been developed for monitoring internal and external tissue biomechanics. Moreover, by adopting advanced machine-learning approaches, more insights can be gained from the collected data. In this Review, we provide a comprehensive overview of conformable electronic devices for tissue biomechanics decoding. We discuss basic principles for external and internal tissue decoding, focusing on electromechanical transduction for external tissue decoding and on ultrasonography for internal tissue decoding. Then, we highlight various data analysis methods, including machine-learning algorithms. Finally, we outline challenges and future directions.