  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. To improve work place safety, it is necessary to
assess musculoskeletal and biomechanical risk exposures associated with these
tasks, which requires very accurate 3D pose. Existing approaches mainly utilize
marker-based sensors to collect 3D information. However, these methods are
usually expensive to setup, time-consuming in process, and sensitive to the
surrounding environment. In this study, we propose a multi-view based deep
perceptron approach to address aforementioned limitations. Our approach
consists of two modules: a "view-specific perceptron" network extracts rich
information independently from the image of view, which includes both 2D shape
and hierarchical texture information; while a "multi-view integration" network
synthesizes information from all available views to predict accurate 3D pose.
To fully evaluate our approach, we carried out comprehensive experiments to
compare different variants of our design. The results prove that our approach
achieves comparable performance with former marker-based methods, i.e. an
average error of $14.72 \pm 2.96$ mm on the lifting dataset. The results are
also compared with state-of-the-art methods on HumanEva-I dataset, which
demonstrates the superior performance of our approach.
