A Feature-Level Fusion-Based Multimodal Analysis of Recognition and Classification of Awkward Working Postures in Construction (Record no. 814992)

MARC details
000 -LEADER
fixed length control field 03128aab a2200253 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240216b20232023|||mr||| |||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0733-9364
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Xiahou, Xiaer
9 (RLIN) 880985
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Li, Zirui
9 (RLIN) 880986
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Xia, Jikang
9 (RLIN) 880987
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Zhou, Zhipeng
9 (RLIN) 880988
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Li, Qiming
9 (RLIN) 880989
245 ## - TITLE STATEMENT
Title A Feature-Level Fusion-Based Multimodal Analysis of Recognition and Classification of Awkward Working Postures in Construction
300 ## - PHYSICAL DESCRIPTION
Extent 1-17 p
520 ## - SUMMARY, ETC.
Summary, etc. Developing approaches for recognition and classification of awkward working postures is of great significance for proactive management of safety risks and work-related musculoskeletal disorders (WMSDs) in construction. Previous efforts have concentrated on wearable sensors or computer vision-based monitoring. However, certain limitations need to be further investigated. First, wearable sensor-based studies lack reliability due to vulnerability to environmental interferences. Second, conventional computer vision-based recognition demonstrates classification inaccuracy under adverse environmental conditions, such as insufficient illumination and occlusion. To address the above limitations, this study presents an innovative and automated approach for recognizing and classifying awkward working postures. This approach leverages multimodal data collected from various sensors and apparatuses, allowing for a comprehensive analysis of different modalities. A feature-level fusion strategy is employed to train deep learning-based networks, including a multilayer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM). Among these networks, the LSTM model achieves optimal performance, with an impressive accuracy of 99.6% and an F1-score of 99.7%. A comparison of metrics between single-modality and multimodal-fused training methods demonstrates that the incorporation of multimodal fusion significantly enhances the classification performance. Furthermore, the study examines the performance of the LSTM network under adverse environmental conditions. The accuracy of the model remains consistently above 90% in such conditions, indicating that the model’s generalizability is enhanced through the multimodal fusion strategy. In conclusion, this study mainly contributes to the body of knowledge on proactive prevention for safety and health risks in the construction industry by offering an automated approach with excellent adaptability in adverse conditions. Moreover, this innovative attempt integrating diverse data through multimodal fusion may provide inspiration for future studies to achieve advancements.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Awkward Working Postures
9 (RLIN) 880990
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Wearable Sensors
9 (RLIN) 878376
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Multimodal Fusion
9 (RLIN) 720247
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Deep Learning
9 (RLIN) 166900
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Risk Management
773 0# - HOST ITEM ENTRY
Place, publisher, and date of publication Reston,Virginia, U.S.A : American Society of Civil Engineers/ American Concrete Institute
International Standard Serial Number 07339364
Title ASCE: Journal of Construction Engineering and Management
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1061/JCEMD4.COENG-13795">https://doi.org/10.1061/JCEMD4.COENG-13795</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Articles
-- 14993
-- Mr. Muhammad Rafique Al Haj Rajab Ali (Late)
Holdings
Not for loan Home library Serial Enumeration / chronology Total Checkouts Date last seen Koha item type
  Periodical Section Vol.149, No.12 (December 2023)   16/02/2024 Articles