@article{xie_li_xu_2022, title={Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer}, volume={64}, ISSN={["1547-8181"]}, url={https://doi.org/10.1177/0018720821995000}, DOI={10.1177/0018720821995000}, abstractNote={Objective We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU). }, number={8}, journal={HUMAN FACTORS}, publisher={SAGE Publications}, author={Xie, Ziyang and Li, Li and Xu, Xu}, year={2022}, month={Dec}, pages={1412–1428} }
 @article{li_prabhu_xie_wang_lu_xu_2021, title={Lifting Posture Prediction With Generative Models for Improving Occupational Safety}, volume={51}, ISSN={["2168-2305"]}, url={https://doi.org/10.1109/THMS.2021.3102511}, DOI={10.1109/THMS.2021.3102511}, abstractNote={Lifting tasks have been identified to be highly associated with work-related low back pain. Posture prediction can be used for simulating workers’ posture of lifting tasks and thus facilitate the prevention of low back pain (LBP). This study adopts two generative models, conditional variational encoder and conditional generative adversarial network, to predict lifting postures. A regular feed-forward neural network (FNN) developed upon previous studies is also investigated for comparison purposes. Ground-truth lifting posture data collected by a motion capture system is used for training and testing the models. The models are trained with datasets of different size and loss functions, and the results are compared. The conditional variational autoencoder and the regular FNN achieved comparable top performance in lifting posture prediction in terms of accuracy and posture validity. Both generative models are able to partially capture the variability of constrained postures. Overall, the results prove that using a generative model is able to predict postures with reasonable accuracy and validity (RMSE of coordinates = 0.049 m; RMSE of joint angles = 19.58$^\circ$). The predicted postures can support biomechanical analysis and ergonomics assessment of a lifting task to reduce the risk of low back injuries.}, number={5}, journal={IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Li, Li and Prabhu, Saiesh and Xie, Ziyang and Wang, Hanwen and Lu, Lu and Xu, Xu}, year={2021}, month={Oct}, pages={494–503} }
 @article{li_xie_xu_2020, title={MOPED25: A multimodal dataset of full-body pose and motion in occupational tasks}, volume={113}, ISSN={["1873-2380"]}, DOI={10.1016/j.jbiomech.2020.110086}, abstractNote={In recent years, there has been a trend of using images and deep neural network-based computer vision algorithms to perform postural evaluation in workplace safety and ergonomics community. The performance of the computer vision algorithms, however, heavily relies on the generalizability of the posture dataset that was used for algorithm training. Current open-access posture datasets from the computer vision community mainly focus on the pose and motion of daily activities and lack the context in workplaces. In this study, a new posture dataset named, MOPED25 (Multimodal Occupational Posture Dataset with 25 tasks) is presented. This dataset includes full-body kinematics data and the synchronized videos of 11 participants, performing commonly seen tasks at workplaces. All the data has been made publicly available online. This dataset can serve as a benchmark for developing more robust computer vision algorithms for postural evaluation at workplaces.}, journal={JOURNAL OF BIOMECHANICS}, author={Li, Li and Xie, Ziyang and Xu, Xu}, year={2020}, month={Dec} }