Wenqiang (Winston) Chen
Current Institution: University of Virginia
Bio: Wenqiang (Winston) Chen is a Ph.D. candidate at the University of Virginia working with Professor John Stankovic. His research lies at the intersection of Cyber-Physical Systems (CPS), ubiquitous and mobile computing, and human-computer interaction (HCI). In particular, his research specializes in developing Vibration Interaction (VibInt) systems to perceive and infer information from human bodies, robots, and environments through vibrations. VibInt has been proposed to advance a wide variety of research areas, such as wearable interactions, robotics, smart health, smart homes, privacy and security. He has published his research in various top conferences and journals (e.g., Mobicom, Ubicomp, and Transactions on Mobile Computing), obtained five patents, and won the IEEE SECON 2018 Best Paper Award and the ACM SenSys 2020 Best Demo Award. Winston is also a co-founder of VibInt AI, a startup working on wearable devices using VibInt technologies, and his research IPs have been used in thousands of commodity devices. You can find out more about him at https://www.cs.virginia.edu/~wc5qd/
Abstract: Harnessing Vibrations for Intelligent Interactions in the Real World
Vibrations convey rich information about interactions through people, objects, and environments. However, vibration technologies are significantly under-explored. Taking advantage of ubiquitous sensors and artificial intelligence, I have developed systems to sense, understand, and broker universal low volume vibrational interchange between people and the physical world.
My Phd dissertation focus on capturing subtle human body vibrations to translate freestyle finger writing and typing for interaction with wearable computing devices such as smartwatches and smart glasses. Challenges addressed include difficulties collecting and labeling a large vibration dataset, filtering human activity noise from finger typing and writing vibration signals through signal processing, designing a novel adversarial neural network to overcome within and between human variations such as those of typing strength, writing style, hand shape, smartwatch wrist position, and finally adopting recurrent neural aligner for continuous recognition rather than discrete gesture type.
To further explore the power of vibrations for fine-grained sensing, I also detect vibrations from buildings, robots and environments for use in ubiquitous computing applications such as Metaverse, robotic automation, smart health, smart home tech, security and privacy. The mission of my research is to integrate human, cyber, and physical experiences into an intelligent world of vibrational interactions.