Year: 2018
Type: conference-paper
Abstract: 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, timeconsuming 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 ± 2:96 mm on the lifting dataset. The results are also compared with state-of-the-art methods on HumanEva-I dataset [1], which demonstrates the superior performance of our approach.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CHS: Medium: Data Driven Biomechanically Accurate Modeling of Human Gait on Unconstrained Terrain, EAGER: Multi-modal human gait experimentation and analysis on unconstrained terrains, EAGER: Development of Model-Based Active Chair for Proactive Injury Prevention, G5049680191, Cooperative Adaptation and Shaping of Human Motor Control through Unstable Physical Human-Robot Interactions
OpenAlex ID: W2964162455