Computing in Civil Engineering (2011) 2011
DOI: 10.1061/41182(416)47
|View full text |Cite
|
Sign up to set email alerts
|

Computer Vision Techniques for Worker Motion Analysis to Reduce Musculoskeletal Disorders in Construction

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

0
12
0

Year Published

2013
2013
2023
2023

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 27 publications
(13 citation statements)
references
References 13 publications
0
12
0
Order By: Relevance
“…However, among these techniques, the manual method is low-efficient, high-cost, and subjective [5][6][7], and methods with wearable equipment need frequent charging and high application cost, affecting the efficiency of workers [8][9][10]. Overcoming the limitation of traditional methods, computer vision technology is considered to realize intelligent management on construction sites [11][12][13], especially for automatic recognition and monitoring of on-site workers [14,15]. Moreover, previous research indicates that on-site safety performance can be improved by computer vision technology (e.g., detection of safety equipment, motion analysis, and tracking of workers) [16,17].…”
Section: Introductionmentioning
confidence: 99%
“…However, among these techniques, the manual method is low-efficient, high-cost, and subjective [5][6][7], and methods with wearable equipment need frequent charging and high application cost, affecting the efficiency of workers [8][9][10]. Overcoming the limitation of traditional methods, computer vision technology is considered to realize intelligent management on construction sites [11][12][13], especially for automatic recognition and monitoring of on-site workers [14,15]. Moreover, previous research indicates that on-site safety performance can be improved by computer vision technology (e.g., detection of safety equipment, motion analysis, and tracking of workers) [16,17].…”
Section: Introductionmentioning
confidence: 99%
“…Similarly, Seo, Yin, and Lee (2016) used silhouette information extracted using computer vision to identify postures in videos. Li and Lee (2011) developed a method that extracts skeletal models of workers from 2D videos with joint locations that can provide abundant information for ergonomic applications. Mehrizi et al (2017; Mehrizi, Peng, Tang, et al 2018; Mehrizi, Peng, Xu, et al, 2018) demonstrated a computer vision marker-less motion capture method to assess 3D pose, back moments, and joint kinematics of lifting tasks.…”
Section: Introductionmentioning
confidence: 99%
“…Specifically, the proposed approach captures workers' motion data from ordinary video or network surveillance cameras (Li & Lee, 2011;Han et al, 2012b;Han et al, 2013) and an RGB-D sensor (e.g., KINECT sensor) (Han et al, 2012a;. Then, the approach assesses risk factors that can produce excessive physical loads on the human body through a biomechanical analysis using motion data collected from various real-world conditions.…”
Section: Introductionmentioning
confidence: 99%
“…Ray & Teizer (2012) suggested real-time analysis on construction workers' posture using a Kinect range camera to detect non-ergonomic activities. Li and Lee (2011) introduced a computer-vision-based approach to obtain construction workers' motion data from video, and identified unsafe postures and movements to prevent WMSDs by giving feedback to the workers. However, a precipitation of WMSDs is an interactive process of biomechanical and physiological internal responses of the human body to external physical stresses (e.g., posture, exertion, and vibration) during occupational tasks (Kumar, 2001).…”
Section: Introductionmentioning
confidence: 99%