Medical Engineering Thesis Defense, Changhao Xu
B270 Moore and https://caltech.zoom.us/j/2057682509
Abstract: Skin-interfaced electronics is gradually transforming robotic and medical fields by enabling noninvasive and continuous monitoring of physiological and biochemical information. Despite their promise, current wearable technologies face challenges in several disciplines: Physical sensors are prone to motion-induced noise and lack the capability for effective disease detection, while existing wearable biochemical sensors suffer from operational instability in biofluids, limiting their practicality. Conventional electronic skin contains only a limited category of sensors that are not sufficient for practical applications, and conventional data processing methods for these wearables necessitate manual intervention to filter noise and decipher health-related information. This thesis presents advances in electronic skin within robotics and healthcare, emphasizing multimodal sensing and data analysis through machine intelligence. Chapter 1 introduces the concept of electronic skin, outlining the emerging sensor technologies and a general machine learning pipeline for data processing. Chapter 2 details the development of multimodal physiological and biochemical sensors that enable long-term continuous monitoring with high sensitivity and stability. Chapter 3 explores the application of integrated electronic skin in robotics, prosthetics, and human machine interactions. Chapter 4 showcases practical implementations of integrated electronic skin with robust sensors for wound monitoring and treatment. Chapter 5 highlights the transformative deployment of artificial intelligence in deconvoluting health profiles on mental health. The last chapter, Chapter 6, delves into the challenges and prospects of artificial intelligence-powered electronic skins, offering predictions for the evolution of smart electronic skins. We envision that multimodal sensing and machine intelligence in electronic skin could significantly advance the field of human machine interactions and personalized healthcare.
Advised by Professor Wei Gao.