@article{li_martin_xu_2020, title={A novel vision-based real-time method for evaluating postural risk factors associated with musculoskeletal disorders}, volume={87}, ISSN={["1872-9126"]}, DOI={10.1016/j.apergo.2020.103138}, abstractNote={Real-time risk assessment for work-related musculoskeletal disorders (MSD) has been a challenging research problem. Previous methods such as using depth cameras suffered from limited visual range and wearable sensors could cause intrusiveness to the workers, both of which are less feasible for long-run on-site applications. This document examines a novel end-to-end implementation of a deep learning-based algorithm for rapid upper limb assessment (RULA). The algorithm takes normal RGB images as input and outputs the RULA action level, which is a further division of RULA grand score. Lifting postures collected in laboratory and posture data from Human 3.6 (a public human pose dataset) were used for training and evaluating the algorithm. Overall, the algorithm achieved 93% accuracy and 29 frames per second efficiency for detecting the RULA action level. The results also indicate that using data augmentation (a strategy to diversify the training data) can significantly improve the robustness of the model. The proposed method demonstrates its high potential for real-time on-site risk assessment for the prevention of work-related MSD. A demo video can be found at https://github.com/LLDavid/RULA_2DImage.}, journal={APPLIED ERGONOMICS}, author={Li, Li and Martin, Tara and Xu, Xu}, year={2020}, month={Sep} }
 @article{li_hutmacher_xu_2019, title={Video-Based Driver's Hand Tracking using Fast Normalized Cross Coefficient with Improved Computational Efficiency}, volume={2673}, ISSN={["2169-4052"]}, DOI={10.1177/0361198119841554}, abstractNote={ Driver distraction is one of the major causes for fatal car accidents. In a distracting activity, manual distraction is a triggered response of other types of distraction, such as cognitive and visual distraction. Therefore, recognition of manual distraction can contribute to the monitoring of overall drivers’ distraction. In this study, a computer vision-based method to track hand movement from the recorded driving behavior is proposed. This method integrates a low computational cost template matching algorithm using fast normalized cross coefficient (NCC) and a novel searching strategy. The proposed method was evaluated by the VIVA hand tracking data set. It achieves 50.83% of marginal accuracy percentage (mAP), 42.18% of multiple object tracking accuracy (MOTA), 31.56% of mostly tracked (MT), and 19.29% of mostly lost (ML), and it outperformed a state of the art algorithm in MOTA and MT. Additionally, the computational cost of the proposed method is greatly improved, and it can run at around 11.1 frames per second. The outcome of this research will further assist driving distraction recognition and mitigation, and improve driving safety. }, number={8}, journal={TRANSPORTATION RESEARCH RECORD}, author={Li, Li and Hutmacher, Clayton M., Jr. and Xu, Xu}, year={2019}, month={Aug}, pages={233–241} }
 @article{li_andersen_heber_zhang_2007, title={Non-monotonic dose-response relationship in steroid hormone receptor-mediated gene expression}, volume={38}, ISSN={["1479-6813"]}, DOI={10.1677/JME-07-0003}, abstractNote={Steroid hormone receptors are the targets of many environmental endocrine active chemicals (EACs) and synthetic drugs used in hormone therapy. While most of these chemical compounds have a unidirectional and monotonic effect, certain EACs can display non-monotonic dose–response behaviors and some synthetic drugs are selective endocrine modulators. Mechanisms underlying these complex endocrine behaviors have not been fully understood. By formulating an ordinary differential equation-based computational model, we investigated in this study the steady-state dose–response behavior of exogenous steroid ligands in an endogenous hormonal background under various parameter conditions. Our simulation revealed that non-monotonic dose–responses in gene expression can arise within the classical genomic framework of steroid signaling. Specifically, when the exogenous ligand is an agonist, a U-shaped dose–response appears as a result of the inherently nonlinear process of receptor homodimerization. This U-shaped dose–response curve can be further modulated by mixed-ligand heterodimers formed between endogenous ligand-bound and exogenous ligand-bound receptor monomers. When the heterodimer is transcriptionally inactive or repressive, the magnitude of U-shape increases; conversely, when the heterodimer is transcriptionally active, the magnitude of U-shape decreases. Additionally, we found that an inverted U-shaped dose–response can arise when the heterodimer is a strong transcription activator regardless of whether the exogenous ligand is an agonist or antagonist. Our work provides a novel mechanism for non-monotonic, particularly U-shaped, dose–response behaviors observed with certain steroid mimics, and may help not only understand how selective steroid receptor modulators work but also improve risk assessment for EACs.}, number={5-6}, journal={JOURNAL OF MOLECULAR ENDOCRINOLOGY}, author={Li, Li and Andersen, Melvin E. and Heber, Steffen and Zhang, Qiang}, year={2007}, pages={569–585} }