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2019.10.11

【Awards and Commendations】Koya Narumi (D3), Department of Information and Communication Engineering, Fang Qin (Master course student), Siyuan Liu (Ph.D. student), Jianzhe Gu (Ph.D. student), Professor Mohammad Islam, Assistant Professor Lining Yao, Carnegie Mellon University, Professor Yoshihiro Kawahara, Department of Electrical Engineering and Information Systems, won Best Paper Award in The 27th Workshop on Interactive Systems and Software (WISS2019).

The paper titled "Self-healing UI: Sensing Interfaces that can Self-heal Mechanical and Electrical Damage" won Best Paper Award (1 paper out of 16 papers) in The 27th Workshop on Interactive Systems and Software (WISS2019).

Living things in nature have long been utilizing the ability to “heal” their wounds on the soft bodies to survive in the outer environment. In order to impart this self-healing property to our daily life interface, we proposed Self-healing UI, a soft-bodied interface that can intrinsically self-heal damages without external stimuli or glue. The key material to achieving Self-healing UI is MWCNTs-PBS, a composite material of a self-healing polymer polyborosiloxane (PBS) and a filler material multi-walled carbon nanotubes (MWCNTs), which retains mechanical and electrical self-healability. We developed a hybrid model that combines PBS, MWCNTs-PBS, and other common soft materials including fabric and silicone to build interface devices with self-healing, sensing, and actuation capability. These devices were implemented by layer-by-layer stacking fabrication without glue or any post-processing, by leveraging the materials’ inherent self-healing property between two layers. We then demonstrated sensing primitives and interactive applications that extend the design space of shape-changing interfaces with their ability to transform, conform, reconfigure, heal, and fuse, which we believe can enrich the toolbox of human-computer interaction (HCI).

Collaboration work is always hard but fruitful, which makes us exciting.

https://www.wiss.org/WISS2019/award