Predicting 3-D Lower Back Joint Load in Lifting: A Deep Pose Estimation Approach
Predicting 3-D Lower Back Joint Load in Lifting: A Deep Pose Estimation Approach
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DOI：
10.1109/thms.2018.2884811
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发表时间：
2019-02-01
影响因子：
3.6
通讯作者：
Li, Kang
中科院分区：
文献类型：
--
作者：
Mehrizi, Rahil;Peng, Xi;Li, Kang
目标：起重是在工作场所进行的一项常见的手动物料搬运任务。它被认为是与工作有关的肌肉骨骼疾病的主要危险因素之一。识别不安全举升任务的一个重要标准是L5/S1关节处的净力和力矩值。这些值主要在实验室环境中计算，利用基于标记的传感器收集三维（3-D）信息和力板测量外力和力矩。然而，这种方法通常设置昂贵，耗时，并且对周围环境敏感。在这项研究中，我们提出了一个基于深度神经网络（DNN）的三维姿态估计框架，解决了上述局限性，并将结果用于L5/S1的力矩和力计算。方法：在该框架的第一步，利用深度神经网络捕获全身三维姿态，然后在第二步，利用估计的三维姿态和受试者的人体测量信息，通过自顶向下的逆动态算法计算L5/S1连接的动力学。结果：为了充分评估我们的方法，我们使用由12名受试者组成的举重数据集进行了实验，这些受试者执行各种类型的举重任务。结果验证了基于标记的运动捕捉系统作为参考。所有数据集的总力矩/力绝对误差的大平均值+/- SD为9.06 +/- 7.60 N.m/4.85 +/- 4.85 N.m。结论：所提出的方法为提升过程中评估下背部动力学提供了可靠的工具，并且在无法使用基于标记的运动捕捉系统时可以作为替代方法。
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 a 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.