Xiangyi Cheng

Assistant Professor of Mechanical Engineering | Loyola Marymount University

Los Angeles, CA, UNITED STATES

Seaver College of Science and Engineering

Biography

Dr. Xiangyi Cheng earned her Ph.D. in Mechanical Engineering from Texas A&M University in 2022 and her B.S. from China University of Mining and Technology-Beijing in 2015. Following her graduation, she served as an Assistant Professor of Mechanical Engineering at Ohio Northern University for two years before joining Loyola Marymount University. Her research focuses on technologies and applications in robotics, augmented reality, and intelligent systems, with the aim of enhancing human-machine interaction and delivering innovative solutions, particularly in the fields of healthcare and education.

Office: East Hall 122
Email: xiangyi.cheng@lmu.edu
Phone: 310.568.6612

Education (2)

Texas A&M University: Ph.D., Mechanical Engineering 2022

China University of Mining and Technology, Beijing: B.S.E., Mechanical Engineering 2015

Areas of Expertise (4)

Intelligent Systems

Robotics

Augmented Reality

Human Computer Interaction (Hci)

Affiliations (2)

Institute of Electrical and Electronics Engineers
American Society for Engineering Education

Links (1)

Lab and Research Website

Articles (4)

Mobile Devices or Head-Mounted Displays: A Comparative Review and Analysis of Augmented Reality in Healthcare

IEEE Access

Ahmed Oun, Nathan Hagerdorn, Caleb Scheideger, Xiangyi Cheng

2024-02-02

Augmented reality (AR), which combines digital rendering with the real world, has significantly shaped the healthcare system. AR technology can be utilized through a range of devices, broadly grouped into two types: mobile devices and head-mounted displays (HMDs). Mobile devices for AR usually include smartphones, tablets, and smartwatches; on the other hand, HMDs include different models of smart glasses and AR headsets. Each device type offers a unique way to experience AR, making the technology accessible and adaptable for various healthcare applications. However, the differences between using mobile devices and HMDs, and which is preferred under specific conditions, have yet to be determined. To address this, the survey provides a comparative review and analysis of the use of mobile- and HMD-based AR in the context of healthcare.

Computerized Block Games for Automated Cognitive Assessment: Development and Evaluation Study

JMIR Serious Games

Xiangyi Cheng, Grover C Gilmore, Alan J Lerner, Kiju Lee

2023-05-16

Cognitive assessment using tangible objects can measure fine motor and hand-eye coordination skills along with other cognitive domains. Administering such tests is often expensive, labor-intensive, and error prone owing to manual recording and potential subjectivity. Automating the administration and scoring processes can address these difficulties while reducing time and cost. e-Cube is a new vision-based, computerized cognitive assessment tool that integrates computational measures of play complexity and item generators to enable automated and adaptive testing. The e-Cube games use a set of cubes, and the system tracks the movements and locations of these cubes as manipulated by the player.

GA-SVM-based facial emotion recognition using facial geometric features

IEEE Sensors Journal

X Liu, X Cheng, K Lee

2020-10-01

This paper presents a facial emotion recognition technique using two newly defined geometric features, landmark curvature and vectorized landmark. These features are extracted from facial landmarks associated with individual components of facial muscle movements. The presented method combines support vector machine (SVM) based classification with a genetic algorithm (GA) for a multi-attribute optimization problem of feature and parameter selection.

Intubot: Design and prototyping of a robotic intubation device

2018 IEEE International Conference on Robotics and Automation (ICRA)

Xiangyi Cheng, Gaojun Jiang, Kiju Lee, Yehoshua N Laker

2018-05-21

Endotracheal intubation is one of the most common procedures performed worldwide in emergency departments and operating rooms. It is a highly complicated procedure susceptible to failure. This paper presents a robotic prototype, called IntuBot, designed to automate this procedure. The hardware system consists of a stepper motor to steer the stylet in forward and backward motions and two servo motors to generate bending at the stylet tip to navigate through a patient's airway. A real-time vision-based navigation algorithm is also presented to guide the stylet to localize the vocal cords, which is the tubes ultimate target. For pre-clinical testing, we 3D printed and then molded a silicone model of the airway from the mouth to the vocal cords based on a series of actual CT scan images. The prototype was tested for its steering capabilities.