During the 107th anniversary foundation of Tsinghua University, the 36th “Challenge Cup” College Student Curricular Academic Science and Technology Works Competition brought to a successful close, Department of Computer Science and Technology won the first prize.[Details]
ACM CHI 2018,the top-tier conference of Human Computer Interaction was held from April 21 to 26, in Montréal, Canada. Around 3000 professionals from both academia and industry attended the meeting. Three research articles from HCI Group were accepted.[Details]
Pervasive HCI Group have 5 articles accepted, making it the most productive research group around the world. Remarkable, our group got the nomination for the Honorable Mention Award for three consecutive years.[Details]
LightGuide: Directing Visually Impaired People along a Path Using Light Cues
(IMWUT’21) Ciyuan Yang¹, Shuchang Xu¹, Tianyu Yu, Guanhong Liu, Chun Yu*, Yuanchun Shi
This work presents LightGuide, a directional feedback solution that indicates a safe direction of travel via the position of a light within the user’s visual field. We prototyped LightGuide using an LED strip attached to the brim of a cap, and conducted three user studies to explore the effectiveness of LightGuide compared to HapticBag, a state-of-the-art baseline solution that indicates directions through on-shoulder vibrations. Results showed that, with LightGuide, participants turned to target directions in place more quickly and smoothly, and navigated along basic and complex paths more efficiently, smoothly, and accurately than HapticBag. Users’ subjective feedback implied that LightGuide was easy to learn and intuitive to use.
Facilitating Text Entry on Smartphones with QWERTY Keyboard for Users with Parkinson’s Disease
(CHI’21) Yutao Wang, Ao Yu, Xin Yi*, Yuanwei Zhang, Ishan Chatterjee, Shwetak Patel, Yuanchun Shi
QWERTY is the primary smartphone text input keyboard configuration. However, insertion and substitution errors caused by hand tremors, often experienced by users with Parkinson’s disease, can severely affect typing efficiency and user experience. In this paper, we investigated Parkinson’s users’ typing behavior on smartphones. In particular, we identified and compared the typing characteristics generated by users with and without Parkinson’s symptoms. We then proposed an elastic probabilistic model for input prediction. By incorporating both spatial and temporal features, this model generalized the classical statistical decoding algorithm to correct in sertion, substitution and omission errors, while maintaining direct physical interpretation.