Authors
Dutta A; Breloff SP; Dai F; Sinsel EW; Warren CM; Carey RE; Wu JZ
Source
J Constr Eng Manage 2021 Aug; 147(8):04021083
Abstract
Missing data is a common problem in data collection for work-related musculoskeletal disorder (WMSD) risk-assessment studies. It can cause incompleteness of risk indicators, leading to erroneous conclusion on potential risk factors. Previous studies suggested that data fusion is a potential way to solve this issue. This research evaluated the numerical stability of a data fusion technique that applies canonical polyadic decomposition (CPD) for WMSD risk assessment in construction. Two knee WMSD risk-related data sets - three-dimensional (3D) knee rotation (kinematics) and electromyography (EMG) of five knee postural muscles - collected from previous studies were fused for the evaluation. By comparing the consistency performance with and without data fusion, it revealed that for all low to high proportion of missing data (10%-70%) from both kinematics and EMG data sets, the WMSD risk assessment using fused data sets outperformed using unfused kinematics data sets. For large proportions of missing data (>50%) from both kinematics and EMG data sets, better performance was observed by using fused data sets in comparison with unfused EMG data sets. These findings suggest that data fusion using CPD generates a more reliable risk assessment compared with data sets with missing values and therefore is an effective approach for remedying missing data in WMSD risk evaluation.
Keywords
Construction workers; Musculoskeletal disorders; MSD; Occupational exposure; Risk assessment; Safety research;
Author Keywords: Tensor decomposition; Risk assessment; Data fusion; Construction safety
Contact
Fei Dai, Associate Professor, Department. of Civil and Environmental Engineering, West Virginia University, P.O. Box 6103, Morgantown, WV 26506
Document Type
Journal Article
Email Address
fei.dai@mail.wvu.edu
Priority Area
Construction
Source Name
Journal of Construction Engineering and Management
