@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{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). Background Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety. Methods Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver’s right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision. Results The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively. Conclusion The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety. }, 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_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} }