Predicting 3-D Lower Back Joint Load in Lifting: A Deep Pose Estimation Approach

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...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems Vol. 49; no. 1; pp. 85 - 94
Main Authors: Mehrizi, Rahil, Peng, Xi, Metaxas, Dimitris N., Xu, Xu, Zhang, Shaoting, Li, Kang
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Anthropometry
Artificial neural networks
Back
Biomechanics
Deep neural network
Force plates
Hoisting
Kinetic theory
lifting
lower back loading
Materials handling
Mathematical analysis
Motion capture
Neural networks
occupational biomechanics
Pose estimation
Risk analysis
Sensors
Task analysis
Three dimensional bodies
Videos
Workplaces
ISSN:2168-2291, 2168-2305
Online Access:Get full text
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Summary: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.
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ISSN:2168-2291
2168-2305
DOI:10.1109/THMS.2018.2884811