Prof. Dr. Xing-Ce WANG
School of Artificial Intelligence
Beijing Normal University
China
Professor Dr. Xingce Wang works at Beijing Normal University. Her research interest covers virtual reality, machine learning, and medical image processing. She worked as PI of funding with 1 item of national science and technology support project, the 3 item of national natural science foundation project, 2 items of key projects in Beijing, one of Beijing natural science foundation, 1 item of the Ministry of Education of youth fund and 1 postdoctoral fund project. She worked as Co-PI of funding involves ten more funding from NSFC, MOE，MOST. In recent years, she has published more than 80 high-quality SCI/EI journal papers and participated in the publication of many works. She has obtained National Science and Technology Progress Award one time, Science and Technology Progress Award of MOE twice; Science and Technology Progress award of Beijing one time, and Science and Technology Progress award of China Computer Federation one time. She also worked as chairman of the Executive Committee of CYBERGAMES 2008, executive member and chair of CAA2011, and Network Chairman of ISMAR2019. In 2017, she won the Excellence Award in Beijing Teaching Basic Skills Competition. In 2014, she won the first prize, the best teaching plan award and the most popular among students’ award in the teaching basic skills competition of Beijing Normal University. Since 2012, she has been awarded the outstanding young teacher award of Beijing Normal University, the outstanding teacher award of Beijing Normal University, the "Capital Talent" award for three times, and the excellent course award for two times. She was the first "Top ten" class teacher of Beijing Normal University in 2009 and she was awarded as outstanding freshmen tutors twice.
PhysioTreadmill: An Auto-Controlled Treadmill Featuring Physiological-Data-Driven Visual/Audio Feedback
We present an automated treadmill featuring physiological-data-driven feedback-PhysioTreadmill, which allows its user to easily control running settings based on their physical condition and can also motivate them through real-time physiological computing. We developed a robust exercise intensity self-adaptive adjustment algorithm using physiological data processing to adjust the user's physiological state accurately. To make use of users’ physiological data and improve user experience when running on a treadmill, we present a human-computer interaction system based on physiological data named PhysioTredmill (RPT). this system can identify and process physiological data which are captured by the sensor, and then the transformed signal is passed to the treadmill and big screen via the wireless receiver. It can allow the user to change the treadmill’s speed and incline automatically based on certain guidelines and increase the user’s running duration by watching the feedback. Our contributions can be summarized as follows:1) A physiological interactive system that can overcome safety and inconvenience issues for the user to adjust speed and incline while running on a treadmill, and provides new design concepts for exertion games or physiological interactive applications; 2) A robust and accurate algorithm that can process physiological data and transform to signals that treadmills are able to receive; 3) An application running on PRT that can improve the user’s running duration and PRT’s user experience. With PhysioTreadmill, we can ideally increase exercise duration, and enhance exercise performance and safety. Two user studies involving 42 participants showed that PhysioTreadmill is user-friendly and can effectively extend users' training duration.