JoonOh Seo

Computer vision techniques for construction safety and health monitoring

For construction safety and health, continuous monitoring of unsafe conditions and action is essential in order to eliminate potential hazards in a timely manner. As a robust and automated means of field observation, computer vision techniques have been applied for the extraction of safety related information from site images and videos, and regarded as effective solutions complementary to current time-consuming and unreliable manual observational practices. Although some research efforts have been directed toward computer vision-based safety and health monitoring, its application in real practice remains premature due to a number of technical issues and research challenges in terms of reliability, accuracy, and applicability. This paper thus reviews previous attempts in construction applications from both technical and practical perspectives in order to understand the current status of computer vision techniques, which in turn suggests the direction of future research in the field of computer vision-based safety and health monitoring. Specifically, this paper categorizes previous studies into three groups-object detection, object tracking, and action recognition-based on types of information required to evaluate unsafe conditions and acts. The results demonstrate that major research challenges include comprehensive scene understanding, varying tracking accuracy by camera position, and action recognition of multiple equipment and workers. In addition, we identified several practical issues including a lack of task-specific and quantifiable metrics to evaluate the extracted information in safety context, technical obstacles due to dynamic conditions at construction sites and privacy issues. These challenges indicate a need for further research in these areas. Accordingly, this paper provides researchers insights into advancing knowledge and techniques for computer vision-based safety and health monitoring, and offers fresh opportunities and considerations to practitioners in understanding and adopting the techniques.

Motion Data-Driven Biomechanical Analysis during Construction Tasks on Sites

AbstractWork-related musculoskeletal disorders (WMSDs) are one of the major health issues that workers frequently experience due to awkward postures or forceful exertions during construction tasks. Among available job analysis methods, biomechanical models have been widely applied to assess musculoskeletal risks that may contribute to the development of WMSDs based on motion data during occupational tasks. Recently, with the advent of vision-based motion capture approaches, it has become possible to collect the motion data required for biomechanical analysis under real conditions. However, vision-based motion capture approaches have not been applied to biomechanical analysis because of compatibility issues in body models of the motion data and computerized biomechanical analysis tools. To address this issue, automated data processing is focused on to convert motion data into available data in existing biomechanical analysis tools, given the BVH motion data from vision-based approaches. To examine the feas...

An Automated Biomechanical Simulation Approach to Ergonomic Job Analysis for Workplace Design

AbstractWork-related musculoskeletal disorders (WMSDs) are reported to be the most common category of nonfatal occupational injuries that result in days away from work and are also a leading cause of temporary and permanent disability. One of the most effective approaches to preventing WMSDs is to evaluate ergonomics considerations early in the design and construction planning stage before the worker encounters the unsafe conditions. However, a lack of tools for identifying potential ergonomic risks in a proposed workplace design has led to difficulties in integrating safety and health into workplace design practice. In an effort to address this issue, this study explores a motion data-driven framework for ergonomic analysis that automates and visualizes the evaluation process in a virtual workplace. This is accomplished by coupling the ergonomic analysis with three-dimensional (3D) virtual visualization of the work environment. The proposed approach uses motion data from the 3D model of the jobsite to ev...

A Stereo Vision-Based Approach to Marker-Less Motion Capture for On-Site Kinematic Modeling of Construction Worker Tasks

Marker-less motion capture has been extensively studied in recent years as a means of evaluating productivity, safety, and workplace design for manual operations on-site. These technologies are ideal for circumstances in which traditional motion capture systems are ineffective due to the need for a laboratory setting and movement-inhibiting markers or sensors. However, many marker-less motion capture systems rely on RGB-D sensors that have limited range and susceptibility to interference from sunlight and ferromagnetic radiation, making them unsuitable for modeling worker actions on construction sites. To address this issue, we propose a marker-less motion capture approach utilizing optical images and depth data obtained from stereo vision cameras. Multiple camera lenses and triangulation algorithms generate depths maps similar to those produced by RGB-D sensors, while still utilizing an optical recording process unhindered by potentially harsh construction site conditions. These data are adapted for existing kinematic modeling systems (i.e. iPi Mocap Studio) for 3-D pose estimation. The experiments show that the proposed approach can provide data precision comparable to that of RGB-D-based systems with fewer operational constraints; thus, motion data can be collected where previously developed methods fail due to environmental or maneuverability restrictions. With the proposed approach, kinematic modeling of human movements can be carried out on construction sites without inhibiting the mobility of the recorded subject.

Simulation-Based Assessment of Workers' Muscle Fatigue and Its Impact on Construction Operations

AbstractConstruction workers are frequently exposed to excessive physical demands due to repetitive lifting and material handling while performing tasks. Consequently, many construction workers suffer from a significant level of muscle fatigue that may negatively impact a project’s performance. Thus, evaluating the level of muscle fatigue prior to work and implementing appropriate interventions to reduce physical demands will help to prevent adverse effects of workers’ fatigue on construction operations. Even though several research efforts have suggested methodologies to evaluate muscle fatigue, the extent to which workers’ muscle fatigue would affect construction performance has not yet been fully studied. To address this issue, a simulation-based framework is proposed to estimate physical demands and corresponding muscle fatigue, and thus to quantitatively evaluate the impact of muscle fatigue during construction operations. Specifically, physical demands from a planned operation modeled using discrete...

Dynamic biomechanical simulation for identifying risk factors for work-related musculoskeletal disorders during construction tasks

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.

Motion-data-driven unsafe pose identification through biomechanical analysis

About 33% of non-fatal occupational injuries and illness are due to workrelated musculoskeletal disorders (WMSDs), which had the highest percentage of injuries or illnesses in construction in 2009. Though techniques previously used to prevent WMSDs (e.g., ergonomic rules, checklists based on surveys, and laboratory experiments) provide valuable insights into the prevention of WMSDs, these techniques may not be suitable for measuring the physical demands required for manual ongoing works under real conditions. In an effort to address this issue, we propose a motion capture approach to obtaining a worker’s posture information for measuring physical loads on body parts (e.g., shoulder, back). The human postures extracted by motion capture are expressed by rotation angles between body joints, and these angles are then converted to joint angles, which are the inputs for biomechanical analysis. The resulting information contains the internal forces of body parts (e.g., hands and feet), and is capable of identifying allowable strength ranges, thereby helping determine overexertion and awkward postures during a task. As a test case, motion data for ladder climbing is obtained using a motion capture system (e.g., Microsoft Kinect) in a laboratory, and the postures are biomechanically analyzed frame by frame. The results show that the proposed method performs well at computing the physical loads, which promises its great potential to understand the causes of WMSDs during ongoing manual construction work. Further, it can automatically predict potentially hazardous postures on certain body parts, which has otherwise only been achieved by tedious and time-consuming manual investigation.

Construction operation simulation reflecting workers' muscle fatigue

Discrete event simulation (DES) is widely regarded as an effective tool for modeling, analyzing and establishing the correct design of construction operations, including the proper quantity and sequencing of resources within the context of a selected field construction method. However, a gap exists in current construction operation studies with respect to human factors, specifically, workers’ physical aspects. Construction workers, one of the most important resources in construction operation, have limited physical capabilities due to fatigue depending on individualand task-related factors. However, less attention to fatigue has been paid in DES studies in construction, despite its significant impacts on construction performance. To understand dynamic impacts of workers’ physical constraints on construction performance, we propose worker-oriented construction operation simulation integrating a fatigue model into discrete event simulation. Specifically, workers’ activities during construction operations are modeled using DES in an elemental task level. Then, physical demands from the operation are estimated through biomechanical analysis on elemental tasks. Fatigue, which refers to diminished physical capabilities under given physical demands, is predicted using a fatigue model, and its impact is subsequently reflected in DES. As a preliminary study, we tested the feasibility of the proposed approach on masonry work by varying crew size. The results indicate that excessive physical demands beyond workers’ capabilities result in productivity losses. Ultimately, the proposed approach has the potential to decide alternatives for construction operation planning to secure high productivity without compromising workers’ health.

Recognizing diverse construction activities in site images via relevance networks of construction-related objects detected by convolutional neural networks

AbstractTimely and overall knowledge of the states and resource allocation of diverse activities on construction sites is critical to resource leveling, progress tracking, and productivity analysis...

Effects of different weights and lifting postures on balance control following repetitive lifting tasks in construction workers

Purpose Repetitive lifting tasks have detrimental effects upon balance control and may contribute toward fall injuries, yet despite this causal linkage, risk factors involved remain elusive. The purpose of this paper is to evaluate the effects of different weights and lifting postures on balance control using simulated repetitive lifting tasks. Design/methodology/approach In total, 20 healthy male participants underwent balance control assessments before and immediately after a fatiguing repetitive lifting tasks using three different weights in a stoop (ten participants) or a squat (ten participants) lifting posture. Balance control assessments required participants to stand still on a force plate with or without a foam (which simulated an unstable surface) while center of pressure (CoP) displacement parameters on the force plate was measured. Findings Results reveal that: increased weight (but not lifting posture) significantly increases CoP parameters; stoop and squat lifting postures performed until subjective fatigue induce a similar increase in CoP parameters; and fatigue adversely effected the participant’s balance control on an unstable surface vis-a-vis a stable surface. Findings suggest that repetitive lifting of heavier weights would significantly jeopardize individuals’ balance control on unstable supporting surfaces, which may heighten the risk of falls. Originality/value This research offers an entirely new and novel approach to measuring the impact that different lifting weights and postures may have upon worker stability and consequential fall incidents that may arise.