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

Li, Chunxia; Lee, Sang Hyun

doi:10.1061/41182(416)47

Cited by 27 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

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%

Multi-Angle Fusion-Based Safety Status Analysis of Construction Workers

Deng

2021

IJERPH

View full text Add to dashboard Cite

Hazardous accidents often happen in construction sites and bring fatal consequences, and therefore safety management has been a certain dilemma to construction managers for long time. Although computer vision technology has been used on construction sites to identify construction workers and track their movement trajectories for safety management, the detection effect is often influenced by limited coverage of single cameras and occlusion. A multi-angle fusion method applying SURF feature algorithm is proposed to coalesce the information processed by improved GMM (Gaussian Mixed Model) and HOG + SVM (Histogram of Oriented Gradient and Support Vector Machines), identifying the obscured workers and achieving a better detection effect with larger coverage. Workers are tracked in real-time, with their movement trajectory estimated by utilizing Kalman filters and safety status analyzed to offer a prior warning signal. Experimental studies are conducted for validation of the proposed framework for workers’ detection and trajectories estimation, whose result indicates that the framework is able to detect workers and predict their movement trajectories for safety forewarning.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Angle Fusion-Based Safety Status Analysis of Construction Workers

Deng

2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…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%

Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions

Greene¹,

Hu²,

Difranco³

et al. 2018

Hum Factors

View full text Add to dashboard Cite

show abstract

“…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%

Dynamic Biomechanical Simulation for Identifying Risk Factors for Work-Related Musculoskeletal Disorders During Construction Tasks

Seo

Lee²,

Armstrong³

et al. 2013

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building Th

View full text Add to dashboard Cite

We propose a dynamic biomechanical simulation method that uses motion capture to evaluate the risk of Work-related Musculoskeletal Disorders (WMSDs). Statistics show that WMSDs accounted for 33% of all non-fatal occupational injuries and illness in construction in 2009, and were a leading cause of temporary and permanent disability. Present methods rely largely on self-reports from workers, observational techniques, and direct measurements of motion and muscle activity to assess awkward postures, physical loads, repetitiveness, and the duration of exposure. While these methods have helped to prevent WMSDs in construction work, they may not be suitable for estimating the internal tissue loads associated with WMSDs. We propose a dynamic biomechanical simulation method to estimate internal forces and moments at each body joint of construction workers with motion capture data. Particularly, we explore the biomechanical loads by simulating active 3D musculoskeletal models based on measured postures and movements. To demonstrate the feasibility of this approach, we studied a ladder climbing task using a portable ladder under controlled laboratory conditions. Postures and motions were determined with a commercial motion capture system (e.g., VICON). The results were analyzed to investigate the feasibility of identifying risk factors based on biomechanical simulation. The results show that the proposed approach allows us to determine the biomechanical basis for WMSDs, and to identify postures and movements associated with excessive physical demands on each body joint. When combined with marker-less motion capture which is our ongoing work, the proposed approach has the potential to assess an individual's motions and to provide personalized feedback for the purpose of reducing biomechanical loads and WMSD risk in real workplaces.

show abstract

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

Cited by 27 publications

References 13 publications

Multi-Angle Fusion-Based Safety Status Analysis of Construction Workers

Multi-Angle Fusion-Based Safety Status Analysis of Construction Workers

Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions

Dynamic Biomechanical Simulation for Identifying Risk Factors for Work-Related Musculoskeletal Disorders During Construction Tasks

Contact Info

Product

Resources

About