Shuai Ma, Zijun Wei, Feng Tian, Xiangmin Fan, Jianming Zhang, Xiaohui Shen, Zhe Lin, Jin Huang, Radomír Měch, Dimitris Samaras, Hongan Wang (CHI 2019) [PDF]
Honorable Mention Award
Taking a high-quality photo needs composition skill which non-expert users lack. We present SmartEye, a novel mobile system to help users take photos with good compositions in-situ. SmartEye integrates the View Proposal Network (VPN), a deep learning-based model that outputs composition suggestions in real-time, and a novel, interactively updated module (P-Module) that adjusts the VPN outputs to account for personalized composition preferences.
Our previous work has proposed a method to help 2D moving target selection. However, it used a general model trained by all users' data collected. When a new user uses our tool, it may be not suitable for him. So we designed a personalized user modeling method to adapt the general model to a specific user.
Jing Gao, Feng Tian, Junjun Fan, Dakuo Wang, Xiangmin Fan, Yicheng Zhu, Shuai Ma, Jin Huang, Hongan Wang (CHI 2018 Poster) [PDF]
In this work, we explored the feasibility and accuracy of detecting motor impairment in Parkinson’s disease (PD) via implicitly sensing and analyzing users’ everyday interactions with their smartphones. Through a 42 subjects study, our approach achieved an overall accuracy of 88.1% (90.0%/86.4% sensitivity/specificity) in discriminating PD subjects from age-matched healthy controls.
Jin Huang, Shuai Ma, Feng Tian, Xiang Li, Jie Liu, Hongan Wang (SCIENCE CHINA) [To appear]
Gait abnormality is one of the major symptoms of nervous system diseases such as Parkinson’s disease. In the clinic, assessments tools usually require patients to complete a long and tedious testing process under the supervision of a doctor, which is tremendous pressure for both patients and hospitals. We propose a novel system, which integrates identity recognition algorithm, behavior recognition algorithm, and built-in gait detection model to accelerate the clinical diagnosis process.
Yunzhi Li, Liuping Wang, Shuai Ma, Xiangmin Fan, Zijun Wang, Junfeng Jiao, Dakuo Wang (CHI 2019 Workshop) [PDF]
We presents three ongoing research projects that aim to study how to design, develop, and evaluate the systems supporting human-AI interaction in the healthcare domain. Collaborating with the local government administrators, hospitals, clinics and doctors, we get a valuable opportunity to study and improve how AI-empowered technologies are changing people's life in providing or receiving healthcare services in a suburb district in Beijing, China. We hope this work will ground the discussion with other participants in the workshop and build further collaborations with the health informatics community.
In this research, we first conduct a preliminary survey among 114 participants to investigate the limits in current video watching modes. Then, based on the survey results, we present Co-Lighter, a novel tool for video viewing and comment. Co-Lighter supports viewers to watch videos and share feelings about video content in a collaborative way.
Liuping Wang, Xiangmin Fan, Feng Tian, Lingjia Deng, Shuai Ma, Jin Huang, Hongan Wang (CHI 2018 Poster) [PDF]
We present mirrorU, a mobile system that supports users to reflect on and write about their daily emotional experience. While prior work has focused primarily on providing memory triggers or affective cues, mirrorU provides in-situ assessment and interactive feedback to scaffold reflective writing.
We present Upcycle-Chic, a design and visualization environment that allows a user to view possible upcycling solutions for a given piece of old furniture and explore varying design variations. These possible solutions are generated based on design strategies drawn from over 1000 examples on the web and books shared by professionals and hobbyist furniture makers.
We present Chronos, an algorithm framework that improves the performance of gesture recognizers by 1) extracting the continuity information from gesture sequences and, 2) enabling designers to optimize the decision-making rewards. The framework is implemented by integrating a dynamic Bayesian network (DBN) with a partially observable Markov decision process (POMDP).
Pregnant women are seeking emotional support in forums. How would they feel if they were replied by a chatbot? We are investigating users’ engagement when getting emotional support in a pregnant related forum where users don’t know whether the comments are replied by a real user or a chatbot. To build the chatbot, we designed a seq-to-seq model to generate diverse comments based on posts. We Hide the robot in the community to reply to the user's posts, and then evaluated users' engagement.