Recent advances in machine learning have seen a wide range of applications across many fields. When combined with developments in flexible, skin-interfaced pressure sensors, these technologies are driving a new generation of personalized health monitoring. From preventing diabetic foot ulcers to tracking respiratory rate and other vital signs, these systems are advancing smarter and more responsive healthcare solutions. This review presents a comprehensive overview of the latest developments in skin-interfaced flexible pressure sensing, starting from their physical mechanisms and ending at microscale material structure. In addition, advanced machine learning approaches for sensor data processing and interpretation is explored, ranging from the fundamental concepts to more recent deep learning models such as temporal convolutional networks for time series classification. Moreover, a systematic review of recent literature is presented, highlighting the application of machine learning in analyzing signals from flexible pressure sensors. Emerging applications leveraging machine learning techniques to facilitate smart health monitoring and human-machine interfaces are explored. A concluding section outlines the challenges and outlook for these emerging technologies as it relates to the biomedical field. To the best of our knowledge, this is the first review which evaluates the potential integration between skin-interfaced flexible pressure sensors with cutting-edge machine learning models, offering a synergistic perspective on next-generation biomedical applications.
Carreiro et al. (Wed,) studied this question.