1. Journal Papers
(13) Yu X, Cai Y, Yang R, Ma F*, Kim W*, 2025. Revisiting sensor-based intelligent fall risk assessment for older people: a systematic review. Engineering Applications of Artificial Intelligence , 144, 110176 (SCI, JCR: Q1, IF: 7.5)
(12) Yu X, Wang C, Wu W, and Xiong S*, 2025. A Real-time Skeleton-based Fall Detection Algorithm based on Temporal Convolutional Networks andTransformer Encoder. Pervasive and Mobile Computing , 107, 102016 (SCI, JCR: Q2, IF: 3.0)
(11) Yu X, Wan J, An G, Yin X, Xiong S*, 2024. A NovelSemi-supervised Model for Pre-impact Fall Detection with Limited FallData. Engineering Applications of Artificial Intelligence, 132,108469 (SCI, JCR: Q1, IF: 7.5)
(10) Yu X, Park S, Kim D, Kim E, Kim J, Kim W, An Y, XiongS*, (2023). A Practical Wearable Fall Detection System based on TinyConvolutional Neural Networks. Biomedical Signal Processing and Control,86, 105325 (SCI, JCR: Q2, IF: 5.1)
(9) Kim, T., Yu, X.,& Xiong, S.* (2023). A multifactorial fall risk assessment system for olderpeople utilizing a low-cost, markerless Microsoft Kinect. Ergonomics (SCI, JCR: Q3, IF:2.561)
(8) Yu, X., Park, S., &Xiong, S.* (2023). Trunk Range of Motion: A Wearable Sensor-based Test Protocoland Indicator of Fall Risk in Older People. AppliedErgonomics (SCI, JCR: Q2, IF: 3.940)
(7) Koo, B., Yu, X., Lee, S., Yang, S., Kim, D., Xiong, S.,& Kim, Y. (2023). TinyFallNet: A Lightweight Pre-Impact Fall DetectionModel. Sensors, 23(20), 8459 ((SCI, JCR: Q2, IF: 3.847)
(6) Yu, X., Ma, T., Jang, J.,& Xiong, S.* (2022). Data augmentation to address various rotation errorsof wearable sensors for robust pre-impact fall detection. IEEE Journal of Biomedical and Health Informatics (SCI, JCR: Q1,IF: 7.021).
(5) Yu, X., Koo, B., Jang, J.,Kim, Y., & Xiong, S.* (2022). A comprehensive comparison of accuracy andpracticality of different types of algorithms for pre-impact fall detectionusing both young and old adults. Measurement,111785 (SCI, JCR: Q1, IF: 5.131).
(4) Yu, X., Jang, J., &Xiong, S.* (2021). A large-scale open motion dataset (KFall) and benchmarkalgorithms for detecting pre-impact fall of the elderly using wearable inertialsensors. Frontiers in Aging Neuroscience,399 (SCI, JCR: Q1, IF: 5.702).
(3) Yu, X., Qiu, H., &Xiong, S.* (2020). A novel hybrid deep neural network to predict pre-impactfall for older people based on wearable inertial sensors. Frontiers in bioengineering and biotechnology, 8, 63 (SCI, JCR: Q1,IF: 6.064).
(2) Yu, X., & Xiong, S.*(2019). A dynamic time warping based algorithm to evaluate Kinect-enabledhome-based physical rehabilitation exercises for older people. Sensors, 19(13), 2882 (SCI, JCR: Q2, IF:3.847).
(1) Qiu, H., Rehman, R. Z. U., Yu,X., & Xiong, S.* (2018). Application of wearable inertial sensors and anew test battery for distinguishing retrospective fallers from non-fallersamong community-dwelling older people. Scientificreports, 8(1), 1-10 (SCI, JCR: Q2, IF: 4.996).
2. Conference Papers
(1)Yu X, Jang J, Xiong S*,2021. Machine Learning-based Pre-impact Fall Detection and Injury Preventionfor the Elderly with Wearable Inertial Sensors. AHFE2021. July 25-29, 2021. New York, USA. (Best student paperaward)
(2) Yu X*, Niu Y, Zhou X, Wu W, Xue C, Xiong S, 2024. A skeleton-based real-time fall detection system for older people using a single RGB camera and edge computing. 22nd Triennial Congress of the International Ergonomics Association (IEA), August 25-29, 2024. Jeju, South Korea. (Oral Presentation)
(3) Yuqing Cai, Yihan Zhou, Xiaoqun Yu*, Woojoo Kim*. 2025. Identifying Usability Challenges in Text-to-Image AI: A Comprehensive Comparison among Mainstream Platforms. HCI International (June 22-27, 2025), Gothenburg, Sweden. (Full Paper)
(4) Chenfeng Wang, Mengxin Zhang, Yuqing Cai,Zhiyuan Yang, Woojoo Kim*, Xiaoqun Yu*. 2025. A Novel Fall Risk Assessment Approach Using Gait Parameters from a Single RGB Camera: A Preliminary Study. HCI International (June 22-27, 2025), Gothenburg, Sweden. (Full Paper)
