- Phone: 919.513.7205
- Email: email@example.com
- Office: 4351 Fitts-Woolard Hall
Xu Xu is an associate professor whose research mainly focuses on human motion data analysis and theoretical human biomechanical modeling with an emphasis on safety promotion. Xu’s research has been supported by the National Science Foundation (Cyberlearning, NRI, IUSE), the National Institute of Occupational Safety and Health and NC State’s Research and Innovation Seed Funding. He has co-authored more than 80 journal articles and conference proceedings on the aforementioned research topics and received the C. A. Anderson Outstanding Faculty Award and Goodnight Early Career Innovator Award.
Xu teaches undergraduate and graduate courses related to human factors and ergonomics, including ISE352 (Fundamentals of Human-Machine Systems Design), ISE 541 (Occupational Safety Engineering), ISE 544 (Occupational Biomechanics) and ISE 794 (Advanced Biomechanical Modeling).
Prior to joining the NC State faculty, Xu started his career as a postdoctoral research fellow in the School of Public Health at Harvard University. From there he became a research scientist at the Liberty Mutual Research Institute for Safety where he received a best-paper and outstanding scientific contribution awards multiple times.
Xu Xu’s research interests include human factors and ergonomics engineering, occupational biomechanics, optimization-based biomechanical modelling, data mining on human motion data, occupational musculoskeletal injury prevention.
|Ph.D.||Doctor of Philosophy in Industrial Engineering||NC State University||2008|
|MSIE||Master of Science in Industrial Engineering||NC State University||2006|
|BSIE||Bachelor of Science in Industrial Engineering||Tsinghua University||2004|
Honors and Awards
Discover more about Xu Xu
- A Simple Method to Optimally Select Upper-Limb Joint Angle Trajectories from Two Kinect Sensors during the Twisting Task for Posture Analysis
- Liu, P.-L., Chang, C.-C., Li, L., & Xu, X. (2022), SENSORS, 22(19). https://doi.org/10.3390/s22197662
- A mobile platform-based app to assist undergraduate learning of human kinematics in biomechanics courses
- Wang, H., Xie, Z., Lu, L., Su, B., Jung, S., & Xu, X. (2022), JOURNAL OF BIOMECHANICS, 142. https://doi.org/10.1016/j.jbiomech.2022.111243
- Mental stress and safety awareness during human-robot collaboration - Review
- Lu, L., Xie, Z., Wang, H., Li, L., & Xu, X. (2022). [Review of , ]. APPLIED ERGONOMICS, 105. https://doi.org/10.1016/j.apergo.2022.103832
- Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer
- Xie, Z., Li, L., & Xu, X. (2022), HUMAN FACTORS, 64(8), 1412–1428. https://doi.org/10.1177/0018720821995000
- A computer-vision method to estimate joint angles and L5/S1 moments during lifting tasks through a single camera
- Wang, H., Xie, Z., Lu, L., Li, L., & Xu, X. (2021), JOURNAL OF BIOMECHANICS, 129. https://doi.org/10.1016/j.jbiomech.2021.110860
- Lifting Posture Prediction With Generative Models for Improving Occupational Safety
- Li, L., Prabhu, S., Xie, Z., Wang, H., Lu, L., & Xu, X. (2021), IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 51(5), 494–503. https://doi.org/10.1109/THMS.2021.3102511
- A novel vision-based real-time method for evaluating postural risk factors associated with musculoskeletal disorders
- Li, L., Martin, T., & Xu, X. (2020), APPLIED ERGONOMICS, 87. https://doi.org/10.1016/j.apergo.2020.103138
- Detection of driver manual distraction via image-based hand and ear recognition
- Li, L., Zhong, B., Hutmacher, C., Jr., Liang, Y., Horrey, W. J., & Xu, X. (2020), ACCIDENT ANALYSIS AND PREVENTION, 137. https://doi.org/10.1016/j.aap.2020.105432
- MOPED25: A multimodal dataset of full-body pose and motion in occupational tasks
- Li, L., Xie, Z., & Xu, X. (2020), JOURNAL OF BIOMECHANICS, 113. https://doi.org/10.1016/j.jbiomech.2020.110086
- A Deep Neural Network-based method for estimation of 3D lifting motions
- Mehrizi, R., Peng, Xi, Xu, X., Zhang, S., & Li, K. (2019), JOURNAL OF BIOMECHANICS, 84, 87–93. https://doi.org/10.1016/j.jbiomech.2018.12.022