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Creators/Authors contains: "Li, Kang"

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  1. ; ; ; ; ; ; ; ; ( , ACS Applied Electronic Materials)
    Free, publicly-accessible full text available August 24, 2022
  2. ; ; ; ; ( , The 38th International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Advanced Materials)
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  4. ; ; ; ; ( , MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019)
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  5. ; ; ; ( , IEEE Transactions on Emerging Topics in Computational Intelligence)
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  6. ; ; ; ( , IEEE Transactions on Pattern Analysis and Machine Intelligence)
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  7. ; ; ; ; ( , Journal of Biomechanics)
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  8. ; ; ; ; ; ; ; ( , Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging)
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  9. ; ; ; ; ; ( , IEEE Transactions on Human-Machine Systems)
    Goal: Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for work-related musculoskeletal disorders. An important criterion to identify the unsafe lifting task is the values of the net force and moment at L5/S1 joint. These values are mainly calculated in a laboratory environment, which utilizes marker-based sensors to collect three-dimensional (3-D) information and force plates to measure the external forces and moments. However, this method is usually expensive to set up, time-consuming in process, and sensitive to the surrounding environment. In this study, we propose amore »deep neural network (DNN)-based framework for 3-D pose estimation, which addresses the aforementioned limitations, and we employ the results for L5/S1 moment and force calculation. Methods: At the first step of the proposed framework, full body 3-D pose is captured using a DNN, then at the second step, estimated 3-D body pose along with the subject's anthropometric information is utilized to calculate L5/S1 join's kinetic by a top-down inverse dynamic algorithm. Results: To fully evaluate our approach, we conducted experiments using a lifting dataset consisting of 12 subjects performing various types of lifting tasks. The results are validated against a marker-based motion capture system as a reference. The grand mean ± SD of the total moment/force absolute errors across all the dataset was 9.06 ± 7.60 N·m/4.85 ± 4.85 N. Conclusion: The proposed method provides a reliable tool for assessment of the lower back kinetics during lifting and can be an alternative when the use of marker-based motion capture systems is not possible.« less
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  10. ; ; ; ; ; ( , Neuroinformatics)
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