1. 期刊论文(“*”:通讯作者；“_”:学生)
(12) Yu X, Wang C, Wu W, and Xiong S*, 2024. An Efficient Skeleton-based Fall Detection Algorithm using Temporal Convolutional Networks with Transformer Encoder. (In review)
(11) Yu X, Wan J, An G, Yin X, Xiong S*, 2024. A Novel Semi-supervised Model for Pre-impact Fall Detection with Limited Fall Data. Engineering Applications of Artificial Intelligence, 132, 108469 (SCI, JCR: Q1,IF: 8.0, 中科院Top期刊)
(10) Koo, B., Yu, X., Lee, S., Yang, S., Kim, D., Xiong, S., & Kim, Y. (2023). TinyFallNet: A Lightweight Pre-Impact Fall Detection Model. Sensors, 23(20), 8459 (SCI, JCR: Q2, IF: 3.847)).
(9) Yu X, Park S, Kim D, Kim E, Kim J, KimW, An Y, Xiong S*, 2023. A Practical Wearable Fall Detection System based on Tiny Convolutional Neural Networks. Biomedical Signal Processing and Control, 86, 105325 (SCI, JCR: Q2, IF=5.1).
(8) Kim, T., Yu, X., & Xiong, S.* (2023). Amultifactorial fall risk assessment system for older people utilizing alow-cost, markerless Microsoft Kinect. Ergonomics(SCI, JCR: Q3, IF:2.561).
(7) Yu, X., Park, S., & Xiong, S.*(2023). Trunk Range of Motion: A Wearable Sensor-based Test Protocol andIndicator of Fall Risk in Older People. Applied Ergonomics,108, 103963 (SCI,JCR: Q2, IF: 3.940).
(6) Yu, X., Ma, T., Jang, J., & Xiong,S.* (2022). Data augmentation to address various rotation errors of wearablesensors for robust pre-impact fall detection. IEEE Journal of Biomedical and Health Informatics (SCI, JCR: Q1,IF: 7.021, 中科院Top期刊).
(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 benchmark algorithms fordetecting pre-impact fall of the elderly using wearable inertial sensors. 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-impact fall for older peoplebased on wearable inertial sensors. Frontiersin bioengineering and biotechnology, 8, 63 (SCI, JCR: Q1, IF: 6.064).
(2) Yu, X., & Xiong, S.* (2019). Adynamic time warping based algorithm to evaluate Kinect-enabled home-basedphysical 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 a new test battery fordistinguishing retrospective fallers from non-fallers among community-dwellingolder people. Scientific reports,8(1), 1-10 (SCI, JCR: Q2, IF: 4.996).
2. 会议论文
(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. (Full Paper, 最佳学生论文奖)
(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)