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.

了解人体的组织生物力学（物理变形和固有机械性能的变化）在健康监测、疾病诊断和生物工程方面具有相当大的潜力。然而，目前用于解码组织生物力学的工具依赖于与生物组织不兼容的刚性和笨重的设备。这种差异会导致测量不准确，甚至导致接受测量的受试者感到疼痛和不适。为了克服当前工具的局限性，已经开发了用于监测内部和外部组织生物力学的适形电子设备。此外，通过采用先进的机器学习方法，可以从收集的数据中获得更多见解。在这篇综述中，我们全面概述了用于组织生物力学解码的适形电子设备。我们讨论了外部和内部组织解码的基本原理，重点介绍用于外部组织解码的机电转导和用于内部组织解码的超声检查。然后，我们重点介绍了各种数据分析方法，包括机器学习算法。最后，我们概述了挑战和未来方向。