Cover image by NIOSH.
Observation-Based Posture Assessment

Review of Current Practice and

Recommendations for Improvement

DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Institute for Occupational Safety and Health
ii | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement 

This document is in the public domain and may be freely copied or reprinted.

Disclaimer

This guidance document is not a standard or regulation, and it creates no new legal 

obligations. It contains recommendations and descriptions of practices that have 

been shown to enhance observation-based assessment of working posture. The 

recommendations presented are advisory in nature, informational in content, and 

intended to assist occupational safety and health practitioners in providing a safe 

and healthful workplace.

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Suggested Citation

NIOSH [2014]. Observation-based posture assessment: review of current practice 

and recommendations for improvement. By Lowe BD, Weir PL, Andrews DM. Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease 

Control and Prevention, National Institute for Occupational Safety and Health, 

DHHS (NIOSH) Publication No. 2014–131.

DHHS (NIOSH) Publication No. 2014–131

July 2014

Safer • Healthier • PeopleTM
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | iii

EXECUTIVE SUMMARY

This report describes an observational approach for assessing postural stress of the 

trunk and upper limbs that is intended to improve risk analysis for prevention of 

musculoskeletal disorders. The approach is supported by several recent research 

studies. These studies have evaluated how much time it takes observers to classify 

specific trunk and upper limb postures, how frequently observers are likely to make 

posture classification errors, and the magnitude of these errors. The frequency and 

magnitude of posture classification errors depend on how many categories (levels) are available from which to classify the specific posture. Recent studies suggest 

that optimal posture analysis performance is obtained by partitioning trunk flexion range of motion into 4 categories of 30° increments; trunk lateral bend into 3 

categories of 15° increments; shoulder flexion into 5 categories of 30°; shoulder 

abduction into 5 categories of 30°; and elbow flexion into 4 categories of 30°. These 

categories are suggested because they optimize how rapidly and effectively analysts can visually judge posture. This report also presents more general guidelines 

for the video recording of posture and for the posture analysis process. Guidelines 

for video recording address such factors as camera position, field of view, lighting, 

and duration of recording. Guidelines for posture analysis address enhancements 

such as the benefits of digital video, computer software, training, and use of visual 

reference and perspective cues. Information in this report can assist health/safety, ergonomics, and risk management/loss control practitioners who conduct job/

worksite assessments of lifting, pushing, pulling, carrying, and/or manual handling 

risk factors.

Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | v

CONTENTS

Executive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Glossary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Current Practices in Job Analysis or MSD Prevention. . . . . . . . . . . . . . 2

Optimizing Observation-based Posture Assessment Performance . . . 4

Additional Recommendations for Observation-Based 

 Posture Recording and Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Video Recording. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Posture Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Appendix B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
vi | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement 

GLOSSARY

Deferred posture analysis: Later analysis of body posture data collected (such as 

on video) in the workplace. This method lends itself to more detailed assessment 

because it allows many postures and events to be observed at the individual level. 

Electrogoniometer: A device to quantify, in analog or digital form, an angle and 

changes of angles between body segments connected by a joint.

Mono-task work: An activity characterized by repeated stereotypical motions and 

exertions, without variation, usually associated with a repeating work cycle of short 

duration.

Parallax: A shift in the apparent relationship in position of an object when viewed 

along a different line of sight.

Posture analysis: Decision-making about the magnitude of a posture, relative to a 

convention specified in the tool or method used. For example, video can be used to 

record or collect body postures in the workplace. These postures can be analyzed later 

with software to determine the angle of the body segments, as viewed on the video.

Posture category: Any of multiple discrete intervals of angular position, usually 

defined by lines and/or arcs, into which a joint range of motion is partitioned.

Posture collection: The recording of postures in the workplace. 

Real-time posture analysis: Observation, collection (via paper checklists or handheld devices), and analysis of body postures in the workplace while tasks are being 

performed (that is, in real time). Real-time posture analysis is likely to provide less 

detail because fewer events can be recorded simultaneously and the frequency with 

which dynamic events can be visually discriminated is lower.

Peak and cumulative posture assessment: Assessments of posture(s) associated with specific events within a task or job, typically to address the most severe 

posture adopted or the posture associated with the greatest load experienced by 

the worker. Cumulative assessments consider how the effect of posture and force 

accumulates over a specific period of work time. Note that cumulative assessments 

can be made of a single task or for all tasks that a job comprises, whether those tasks 

are the same (repetitive) or variable (nonrepetitive). 

Variable work: Workplace tasks that are characterized by motions and exertions 

that are noncyclical and without a defined work cycle.
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | vii

ACKNOWLEDGMENTS

This report was developed by Brian Lowe (NIOSH, Cincinnati, Ohio), Patricia 

Weir (University of Windsor, Ontario, Canada), and David Andrews (University 

of Windsor, Ontario, Canada). The authors gratefully acknowledge the support of 

their institutions in addition to that provided by the Centre of Research Expertise 

for the Prevention of Musculoskeletal Disorders (Canada) and the AUTO21 Network of Centres of Excellence. 

The authors acknowledge the contributions of Vanessa Williams (NIOSH) for 

graphic design Seleen Collins (NIOSH) for copyediting, and Jim Albers (NIOSH) 

for the cover photo.

Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 1

BACKGROUND

The purpose of this document is to help practitioners assess working posture for the prevention and control of occupational musculoskeletal disorders (MSDs). Quantitative or 

semiquantitative descriptions of posture are 

inputs to many job analysis tools applied in 

MSD prevention and control. Studies of the 

relationship between risk factors (such as posture, repetition, and force) and resulting MSD 

prevalence have used various approaches to 

characterizing working posture, including 

observation-based methods. Posture classification by systematic observation of a worker 

is commonly used in research and by practitioners, such as ergonomists, industrial hygienists, and safety professionals, to help inform job design decisions and establish safe 

work limits to reduce MSD injury risk in the 

workplace. 

Just as direct measurement methods have limitations in their ability to accurately assess exposure, it is equally important to consider the limitations of an observer in discriminating among 

posture categories (levels) that reflect increased 

exposure severity. Some estimation “error” is 

inherent in the use of any observation-based assessment tool. Recent studies have identified an 

approach to the selection of posture categories 

that reduces posture classification errors and improves efficiency, thereby providing an opportunity to improve posture assessment in the workplace. This report presents this recent evidence, 

which forms the basis for an emerging practice to 

optimize observation-based posture assessment 

performance and efficiency. A secondary purpose 

of the document is to help practitioners improve 

posture recording and analysis using observationbased assessment methods (See page 5).

Overexertion injuries to the musculoskeletal system (including those from lifting, 

pushing, pulling, holding, carrying, or throwing) cost U.S. businesses $12.75 billion 

(U.S.) in direct costs in 2009 and accounted for more than a quarter of the overall national burden [Liberty Mutual Research Institute for Safety 2011]. The situation is similar in Canada where a 2005 labor market report estimated direct and indirect cost of 

musculoskeletal disorders (MSDs) at $20 billion (CDN) [McGee et al. 2011]. In Canada, 

26.4% of all injuries at work in 2003 were due to overexertion [Wilkins and Mackenzie 2007]. In Ontario, sprains and strains accounted for 50.2% of lost-time claims, and 

46.6% of these claims were due to events such as overexertion, static postures, and 

repetitive motions [WSIB 2009]. In Manitoba, 60% of all lost time injuries are MSDs. 
2 | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement 

Figure 1. Comparison of worker self-report, observation-based, and direct methods for assessing musculoskeletal disorder risk factors, such as working posture. The green arrows (+) indicate desirable attributes, the 

red arrows (-) undesirable attributes (for example, high validity is desirable [+]; high cost is undesirable [-]).

CURRENT PRACTICES IN JOB ANALYSIS FOR MSD PREVENTION

The goal of job analysis is to proactively identify factors associated with increased risk 

for work-related MSDs. In general, three approaches have been used to identify risk factors: 1) worker self-report, where the worker is 

asked to estimate the risk factor levels associated with his or her work; 2) observation-based 

methods, where a job analyst observes the 

work in real time or from recorded video, with 

a systematic approach to classifying risk factors; and 3) direct measurement, where instrumentation is used to measure posture directly. The relative advantages and disadvantage 

of these approaches can be considered in the 

manner shown in Figure 1 [Kilbom 1994; Winkel and Mathiassen 1994]. Observation-based 

approaches generally yield less valid assessments of risk factors than could be obtained 

by direct methods such as a motion capture 

system or electrogoniometer. However, observation-based methods can cost less, be more 

accessible, require less expertise, and be easier 

to implement for the practitioner in the field. 

It is recognized that the time and resulting cost 

associated with more detailed, video-based 

analysis (that is, deferred analysis) can be high, 

depending on the objectives of the analysis and 

the nature of the work.

A number of practical observation-based methods have been developed to evaluate musculoskeletal risk factors. In a recent review by 

Takala et al. [2010], 30 of the 32 observational approaches in the review assessed posture 

as a risk factor. Specific tools include, but are 

not limited to Rapid Upper Limb Assessment 

(RULA) [McAtamney and Corlett 1993], Rapid Entire Body Assessment (REBA) [Hignett 

Worker self-report Observation-based Direct

Validity

Expertise needed

Cost

Scope of use

Accessibility
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 3

and McAtamney 2000], Strain Index [Moore and 

Garg 1995], Occupational Repetitive Actions Index (OCRA) [Occhipinti 1998], TRAC [van der 

Beek et al. 1992] and other approaches reported 

in scientific publications [Armstrong et al 1982; 

Genaidy et al 1993; Seth et al 1999]. Posture is a 

key input in these analysis tools in which the analyst classifies a body segment position which is 

partitioned into posture categories. Each posture 

category represents a certain portion of the range 

of motion. (Table 1 shows the number of posture 

categories for the methods above.)

Although posture is recognized as a risk factor 

in all of these methods, it is often difficult to 

compare results among studies using the various methods. One reason for this is that postures have not been standardized across the 

methods in the size, and therefore number, of 

posture categories used to quantify working 

posture [Andrews et al. 2008a, 2008b; Keyserling 1986; Juul-Kristensen et al. 2001; Lowe 

2004a; Weir et al. 2011]. One reason for the lack 

of consistency between studies is the nature of 

the job or task and that the characteristics of the 

physical exposures in the job affect the decision 

about type of assessment, sampling approach, 

and summary measures to be adopted. There are 

several types of assessments in which working 

posture is classified on the basis of visual observation, as shown in Appendix A. Though direct

measurement technologies are improving, currently many practitioners are assessing physical 

job demands by way of observational judgment. 

The following section presents posture categories 

for observation-based posture classification that 

have been demonstrated to optimize observer 

performance and efficiency. These categories are 

defined for the spatial description of individual 

posture observations, in which a still image or 

isolated video frame of an event is defined. It is 

beyond the scope of this report to address statistical treatment of posture sampled over time. As 

the science, knowledge base, and measurement 

technologies relevant to posture assessment in 

MSD prevention and control are further developed, best practices will continue to evolve.

Table 1. Selected methods for MSD risk assessment and their associated number of posture categories. Each value represents 

the number of categories into which the posture range is partitioned.

Method*

Trunk Shoulder Elbow Forearm Wrist

Flexion

Lateral 

bend Twist

Flexion/

extension

Abduction/ 

adduction Flexion

Pronation/

supination

Flexion/

extension

Radial/

ulnar 

deviation

(1)    5 2 3 4 4 2

(2)        5 2

(3) 4 2 2 2     

(4)    3   2 2 2

(5) 4 2 2 5 2 3 2 4 2

(6)    6 4† 4 3 5 3

(7) 4 2 2 4 2 3 2 5 2

(8) 4   4 4 2 3 5 5

Figure 2, 

present 4 3  5 5 4   

*Method definitions and sources: (1) RULA—McAtamney and Corlett (1993); (2) Strain Index—Moore and Garg (1995); (3) TRAC—

van der Beek et al. (1992); (4) OCRA—Occhipinti (1998); (5) REBA—Hignett and McAtamney (2000); (6) Armstrong et al. (1982); (7) 

Genaidy et al. (1993); (8) Seth et al. (1999). †

Horizontal abduction/adduction. 
4 | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement

OPTIMIZING OBSERVATION-BASED POSTURE

ASSESSMENT PERFORMANCE 

The framework in Figure 2 has been shown to 

optimize assessment performance when consideration is given to posture classification error (how often errors are made and how large 

the errors are) and the speed of posture classification. Recent studies suggest that optimal 

posture analysis performance is obtained by 

partitioning trunk flexion range of motion into 

4 categories of 30° increments; trunk lateral 

bend into 3 categories of 15° increments; shoulder flexion into 5 categories of 30°; shoulder 

abduction into 5 categories of 30°; and elbow 

flexion into 4 categories of 30°. (The research 

background for this framework is described in 

Appendix B.)

Optimal posture 

category sizes Neutral Increasingly non-neutral

Trunk flexion

Four 30° categories

30 ̊

90 ̊

0 ̊

60 ̊

30 ̊

90 ̊

0 ̊

60 ̊

90 ̊

0 ̊ 0 ̊

90 ̊

Trunk lateral bend

Three 15° categories

15 ̊

90 ̊

0 ̊

30 ̊

15 ̊

90 ̊

0 ̊

45 ̊

30 ̊

90 ̊

0 ̊

Shoulder flexion

Five 30° categories

0 ̊ 30 ̊

90 ̊

60 ̊

0 30 ̊  ̊

90 ̊

60 ̊

0 ̊

90 ̊

0 ̊

90 ̊

120 ̊

0 ̊

90 ̊

120 ̊

Shoulder abduction

Five 30°categories

0 ̊

90 ̊

30 ̊ 0 ̊

90 ̊

60 ̊

30 ̊

90 ̊

60 ̊

0 ̊ 0 ̊

90 ̊

120 ̊

0 ̊

90 ̊

120 ̊

Elbow flexion

Four 30° categories

90 ̊

30 ̊ 0 ̊

90 ̊

60 ̊

30 ̊ 0 ̊

60 ̊

0 ̊

90 ̊ 90 ̊

120 ̊

0 ̊

Figure 2. Posture category sizes suggested for trunk flexion, trunk lateral bend, shoulder flexion, shoulder 

abduction, and elbow flexion postures (illustrations from Andrews et al. [2012]). 
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 5

ADDITIONAL RECOMMENDATIONS FOR

OBSERVATION-BASED POSTURE RECORDING AND ANALYSIS

Video Recording

When working postures are video recorded for later analysis (deferred analysis), 

the quality and accuracy of the analysis will depend on video recording practices. 

The following are recommendations for video recording of work postures. 

View

Consider recording 

the task from multiple 

views.

The view of the worker is important and can impact the quality and accuracy of observations made using any method. Tasks that are performed similarly by both sides 

of the body (for example, right and left hands) and which occur mostly in one plane 

may only require a single camera view to capture an accurate sample of working 

postures. Asymmetrical tasks will likely require more views. For symmetrical tasks, 

views that are perpendicular to the main direction of movement provide valuable information. Some analysis methods allow for multiple views to be analyzed [Callaghan 

et al. 2003], which can be helpful when assessing highly asymmetrical tasks or tasks 

where a body segment is obscured by an object or by the worker’s own body within 

a single view. Recording from several cameras at the same time in the workplace will 

provide multiple views, but the same effect can be accomplished, for repetitive tasks, 

by using one camera and recording several cycles from different vantage points. 

Encourage the worker 

to avoid loose-fitting 

clothing.

In addition to physical barriers in the workplace, the clothing of the observed worker 

can interfere with observation-based posture assessments. Loose clothes or thick 

layers of clothing can be problematic for quantifying body postures. In many cases, 

clothing cannot be modified (for example, uniforms or personal protective equipment). However, if it is possible for workers to wear tighter or thinner clothes or 

garments that have less material (for example, short-sleeved vs. long-sleeved shirts 

and shorts vs. pants), it may improve viewing and analysis.

Lighting and Contrast

Consider ways to 

improve lighting in the 

work environment or the 

camera’s ability to deal 

with low light.

The amount of light and contrast between the worker and the work environment can 

affect real-time and deferred video-based posture observations, but it will likely have 

greater impact on video-based approaches. In general, good lighting and contrast 

are helpful and should be evaluated prior to video recording if possible, by taking a 

sample video and reviewing it prior to collecting all work tasks. If the worker moves 

between various environments during the assessment, then tests in each area might 

be needed if lighting and contrast are concerns. Portable lighting (mounted on a camera or tripod) can be used to improve viewing conditions. In advance of recording, 

determine the best positions and amounts of light needed for optimal viewing without interfering with the work being performed. A camera with good low-light capture 

capability may also improve the quality of the resulting low-light video.
6 | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement 

Camera Movement, Stability, and Framing

Minimize unnecessary 

camera movement.

Observation-based posture assessment methods such as RULA [McAtamney and 

Corlett 1993] or 3DMatch [Callaghan et al. 2003] require observers to select posture 

categories that correspond to the actual body postures seen in real-time (RULA) or 

previously recorded video (RULA, 3DMatch). These approaches give the observer the 

freedom to move around the workplace so that an optimal view can be achieved at all 

times. However, posture analysis accuracy can be affected by how stable the camera 

view is. If the task requirements limit the range of motion of the work, a tripod could 

be used to ensure that the camera view remains consistent and smooth. If the worker 

has to move beyond the view of a stationary camera, the camera operator will need 

to move with the worker and try to keep the camera as stable as possible. Camera 

supports, monopods, or even a solid surface on which to rest the camera while it is 

strapped to the hand can help reduce camera shake, which can improve later viewing.

Zoom in on limb segments so that the joint 

of interest is as large as 

possible in the camera 

field of view. 

It is also important to make sure that the body segments being assessed are in full 

view within the frame of the video. It may be difficult, because of obstructions and 

movement in the workplace, to get close to the worker without interfering. Use 

the zoom function to fill the frame as much as possible so that you are located at a 

safe distance and the view is not restricted. Because of the smaller size of the hand 

segment relative to the trunk and arm segments, a zoomed-in view of the hand is 

desirable when observation-based analyses of wrist posture are conducted. For more 

dynamic work activities, this may be difficult, because the hands may be moving in 

space and may move out of the field of view of a fixed-position camera. More posture 

classification errors can be expected when postures of the smaller limb segments and 

joints are estimated.

The best camera position 

is perpendicular to the 

plane of the joint(s) of 

interest. 

Video images represent posture two-dimensionally, which challenges the observer if 

the camera view is not perpendicular to the plane of motion of interest at a specific 

joint. Perspective errors (parallax) can be introduced by the two-dimensional representation of posture in three-dimensional space and by a camera that is not ideally 

positioned with respect to the posture of interest. If the camera view is perpendicular 

to the plane of motion of the joint of interest, then a more accurate assessment of 

the angle can be made. When the camera view is not perpendicular to this plane of 

motion, perspective error may result. However, studies have shown that accurate 

posture classification can be attained in these situations [Sutherland et al. 2007] and 

that estimation error due to parallax is often less than would be predicted by the 

spatial relationship between the camera and the joint observed [Lau and Armstrong 

2011]. Nonetheless, when possible, the camera should be oriented perpendicularly to 

the plane of motion to obtain an ideal view.

Consider acquiring video 

from multiple camera 

positions when an 

optimal view cannot be 

achieved.

If the ideal camera perspective cannot be achieved, then samples of the job can be 

obtained from multiple perspectives-either with two cameras simultaneously or with 

a single camera capturing different perspective views sequentially.
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 7

Duration of Observation

Jobs with more variable 

postures may require 

longer observation 

periods. 

The length of time for which postures should be observed depends on the type of 

task(s) the worker is performing and the nature of the analysis. If the analysis is for 

tasks that require the worker to do the same things repetitively, then observing only 

a few cycles or repetitions of the repetitive task is likely sufficient. Similarly, when 

evaluating the peak stress of a particular task, only a short period of time may need to 

be analyzed. Identifying the task associated with peak stress, particularly when this 

task occurs infrequently, often requires discussion with the worker(s). In cases where 

the work is nonrepetitive or when the cumulative effects of posture exposure are to 

be assessed, the work must be observed over a longer period of time. Generally, the 

more variable the work in terms of posture, the more observation time is needed to 

obtain a representative sample of the posture.

Conduct postural 

assessments of multiple 

workers, with particular 

emphasis on the workers 

exhibiting the most 

severe postures. 

Workers vary in their body size (anthropometry) and work technique. These differences can result in posture and physical stresses that vary among workers performing 

similar (or identical) jobs. It is important to assess posture for multiple workers, 

preferably closer to the extremes in sizes, to ensure that the assessment of posture 

reflects that of the most severe cases.
8 | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement 

Posture Analysis 

The following are practical recommendations to improve analysis of posture from 

a video that has been recorded previously.

The definition of postures simplifies the representation of joint position for the purpose of 

characterizing postural stress. 

Observation-based posture assessment can be enhanced by the 

definitions used for the postures themselves. For example, for the 

purpose of observational assessments, shoulder posture is typically 

simplified by considering a single “humeral-thoracic” joint – the 

angular position of the arm with respect to the trunk. This is a 

biomechanical simplification of the complexity of the shoulder 

girdle, which consists of multiple joints. Similarly, segment motions 

throughout the lumbar and thoracic spine are typically simplified in 

the definition of trunk postures to include trunk flexion/extension 

(or sometimes trunk inclination), trunk lateral bend, and trunk 

rotation. These biomechanical simplifications make it easier to 

visually estimate back posture for prediction of injury risk. They also 

require a compromise in the level of biomechanical detail that can 

be obtained. 

Visual reference of posture angles improves 

performance. 

The observation of posture can be enhanced by providing the analyst 

with visual reference of the joint angles defining the posture categories. These are graphical representations of the posture category 

boundaries. The analyst’s task is then one of matching the observed 

posture to the reference images rather than the more challenging 

task of directly estimating an angle between limb segments. Recent 

work has shown that adding a more salient border, either monochrome or colored, to the posture category diagrams decreases decision time for classifying the posture, in comparison with displaying 

the posture categories without borders [Andrews et al. 2012]. Error 

rates were also lower overall when line borders and shading were 

presented. Adding a border enhances the posture category salience, 

thereby improving the efficiency and accuracy of posture matching. 

Visual cues can assist judgment of posture 

when camera view is not perpendicular to the 

joint motion.

When camera position limits an ideal viewing perspective, estimation of joint angles can be enhanced through the use of visual cues. 

An example would be the use of relative length of the hand and 

fingers to classify wrist posture [Lau and Armstrong 2011]. Wrist 

flexion/extension is not well observed from a dorsal (back side) 

view of the hand (Figure 3). This camera position makes judgment 

of wrist flexion/extension difficult because the flexion/extension 

motion plane is not perpendicular to the camera view. However, a 

change in the hand length with respect to hand width is a length 

cue indicating a flexed or extended wrist. The hand length is shorter 

(relative to hand width) in the right panel. 
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 9

Ideal view

Non-ideal view

Figure 3. Ideal view for estimating wrist flexion (top image) is perpendicular to the joint flexion motion 

plane. In a non-ideal view (bottom images) the observer can use other cues, such as length perspective, 

to identify wrist flexion. The hand appears shorter in the flexed wrist posture, relative to neutral posture.
10 | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement

Consensus of multiple job analysts is more 

accurate than assessment of a single analyst. 

Evidence suggests that a consensus group estimate by multiple 

observers, or an average of their estimates, improves posture 

assessment [Latko 1997]. When multiple observers estimate or rate 

posture severity, the average of these ratings is likely to be more 

accurate than most individual ratings. However, this approach is 

more time and resource intensive because of the need for multiple 

observers, and it will decrease the speed of the posture assessment 

method. It may be feasible in a research application, but it may be 

less practical in an industrial application. 

Software programs and other computer-based 

approaches can assist posture analysis. 

Video of working postures is being collected in digital format routinely, if not exclusively, in the workplace today. Computer software 

applications have been developed to enhance the manipulation 

of digital video for the purpose of posture analysis. For instance, 

computer software programs can be written to calculate two-dimensional angles directly from screen coordinates of mouse clicks 

on anatomical landmarks. If the video image plane is parallel to the 

plane of joint motion, then the software can accurately calculate the 

angle. However, as described previously, perspective errors will be 

introduced when the camera is not perpendicular to the plane of 

joint motion. Computer software programs have been developed 

for an analyst to mark exposure category transitions on a timeline 

synchronized with video playback. Changes in work posture can be 

denoted as exposure category transitions. These software programs 

perform summary calculations of cumulative exposure time for the 

manually identified posture transitions on the timeline [Yen and 

Radwin 1995]. Time study reports can then be generated, which 

show cumulative representations of posture, reflecting the duration 

of exposure to non-neutral postures and/or to inform analyses of 

cumulative load. These software tools enable detailed analyses of 

posture and summaries of results for video segments. However, 

these analyses can be time consuming, and some authors suggest 

that analysis time may be up to 30 times the real-time duration of 

the video segment [Heberger et al. 2012]. 

Training may improve posture classification 

performance. 

Training and experience in ergonomics have been shown to affect 

both decision time and accuracy of posture classification. Training 

has been shown to improve the reliability of industrial inspection 

performance and to decrease the decision time of analysts coding 

postures [Weir et al. 2011]. In both cases, active involvement on the 

part of the inspector/analyst during training was critical to success. 

Inexperienced analysts appear to benefit more from training than 

experienced analysts [Weir et al. 2011], but there is error inherent in 

all perceptual tasks. However, regardless of experience, all analysts 

can make improvements in their performance with practice [Andrews et al. 2008b].
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 11

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Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 13

APPENDIX A 

Types of Job Analyses Based on Observational Assessment of Posture 

Compliance with Ergonomic Guidelines

(Ergonomic Audits) 

Many organizations have established guidelines or 

limits for acceptable working postures to reduce 

physical stresses on their workers. Evaluating 

compliance with such guidelines involves observation of work processes and identifying postures 

exceeding an established limit. The observation 

involves screening a task for any postures exceeding the limit. Often a single posture threshold is 

referenced, or jointly observed risk factors may 

be referenced (for example, exertion of force in 

addition to posture exceeding a threshold). 

Example

A company’s ergonomics guideline includes the 

screening for unacceptable wrist posture in combination with force exerted by the hand. Unacceptable 

wrist posture is defined by 50° extension, 75° flexion, 15° ulnar deviation, or 10° radial deviation. The 

occurrence of any of these postures, combined with 

hand force exertion, triggers immediate action by 

way of job redesign. An ergonomics team member 

conducts an assessment of a new process, which 

involves observing work posture when a load is 

handled and identifying the presence of a bent wrist. 

This is done in a real-time analysis.

Another company’s ergonomics program sets an 

exposure limit to the duration of time for working postures in which the trunk is flexed greater 

than 30°, simultaneously with a shoulder flexion 

posture exceeding 45°. This is done as a deferred 

analysis from a video recording of the work, so that 

the slow-motion and freeze-frame features of the 

video playback can be used to assess simultaneous 

postures.

Static Approximation of Biomechanical Loads 

This involves calculation of biomechanical 

forces and moments on the musculoskeletal system 

for specific static working postures. It is typically 

performed when peak levels of biomechanical 

stress are of interest. Often, posture assessments 

are coupled with a measurement of external force 

(for example, load in hands, weight of lifted object). Use of a video recording allows identification 

of a specific exposure event, which can then be 

observed statically for estimates of posture. 

Example

An ergonomist conducts a static biomechanical 

analysis of a worker lifting luggage from a conveyor 

to a screening area. Lifting posture is observed at 

the instant the load is lifted from the surface. Joint 

angles for the posture are estimated from a still 

image from a video recording of the task event, and 

these postural angles are entered in a biomechanical analysis software program to calculate estimates 

of forces and moments on the low back and to 

predict injury risk on the basis of the work posture 

and task conditions.
14 | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement 

Assessment of Exposure to Risk Factors

in Mono-task Work 

This type of assessment is commonly performed 

in the analysis of repetitive, stereotypical motions 

and exertions where a unit of work is completed in 

a short period of time (often less than one minute). 

Assessments are typically based on continuous 

observation of several work cycles, which, because of 

their short duration, can be made in a few minutes. 

It is assumed that variation between work cycles is 

small and that posture exposure assessed for a short 

duration (a few work cycles) can be extrapolated 

over longer work durations, such as the full work 

day. Summary measures include the most frequently 

observed posture, the most extreme posture, and the 

amount of time the working posture was observed 

within specific posture categories. 

Example

A repetitive assembly process is assessed before (in 

its current form) and after a workstation intervention is implemented to reduce reach distances 

to part bins. The Rapid Upper Limb Assessment 

(RULA) method [McAtamney and Corlett 1993] 

is used, and three employees are evaluated with 

both the conventional and modified design. The 

RULA method assesses the posture extremes of the 

upper limbs and the duration of time observed in 

the posture categories (most frequently observed 

posture). 

Assessment of Cumulative Exposure to Risk Factors in 

Variable Work (Non-Mono-Task)

Variable work lacks a cyclic pattern (in the task 

elements, motions, or postures) that would allow 

results from short, continuous observation to be 

meaningfully extrapolated over a full work shift. 

Observation-based approaches are impractical 

for continuous monitoring over long periods. An 

accepted sampling approach to this variability 

is periodic observations to document posture at 

predetermined intervals to statistically infer the 

occurrence of exposure events. Such sampling 

approaches seek to determine the frequency of the 

exposure events (posture categories), such as the 

percentage of work time a joint is observed in a 

non-neutral posture or a cumulative exposure to 

postural stress. Time-sampling approaches can be 

complex and include whole- and partial-interval 

sampling, as well as fixed- and random-interval 

momentary-time sampling. 

Example

A safety specialist is interested in assessing MSD 

risk factors associated with a trenching process. 

The work is observed in real time at fixed intervals, 

and gross postures are documented over the course 

of a 4-hour work period. The analyst observes a 

crew of workers performing the process during 

each sampling period. Observations are made at 

fixed intervals of 60 seconds, and the specific worker for each observation is selected at random from 

the crew, prior to the observation period [Buchholz 

et al. 1996]. 

A similar fixed-interval or momentary-time sampling 

strategy was used to evaluate the effect of a behavioral 

intervention that consisted of providing individual 

feedback on computer users’ working posture [Sasson 

and Austin 2005]. Analysts recorded instances of 

four posture variables (wrist position, neck position, 

back/shoulder position, and feet position). Each of 

these was defined dichotomously as either “safe” or 

“at risk,” depending upon whether or not the joint was 

determined to be aligned with the neutral reference 

position (the “safe” posture). The percentage of safe 

observations was reported as the outcome variable. 

Observations were conducted for each of the four 

posture variables every 16 seconds (four seconds per 

observation), so that 20 observations were collected 

in a five-minute session, twice daily for 52 days. The 

recorded estimates of safe posture over time allowed 

the researchers to evaluate the behavioral effects 

longitudinally.
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 15

APPENDIX B

The Research Background for an Optimal Observation-based Posture Assessment Approach

Observation-based posture assessments rely on 

the observer’s visual discrimination among categories of posture severity to classify posture. 

The number of categories is determined by the 

joint range of motion and the size of the posture categories. Justifications for establishing 

posture category sizes in observation-based 

methods have been varied, ranging from the 

idea that non-neutral postures place a worker 

at risk [Keyserling 1986] to a rationale based on 

muscle force and fatigue [McAtamney and Corlett 1993]. Juul-Kristensen et al. [2001] reviewed 

existing posture assessment methods and concluded that a 45° posture category boundary was 

used frequently because a 45° angle was believed 

to be easily distinguishable. Other approaches 

have considered the likelihood of posture classification error when discriminating among 

multiple posture categories and the size of the 

joint range of motion in establishing the number of posture categories. Lowe [2004a, 2004b] 

assessed upper-limb posture classification accuracy when the range of motion was partitioned 

into three and six categories and showed that the 

likelihood of classification error increased with 

more categories. Other work (for example, the 

3DMatch approach of Callaghan et al. [2003]) 

has accounted for the size of the range of motion 

when partitioning the range into posture categories for the trunk, elbow, and shoulder. 

Given that a goal of posture assessment is to 

optimize both analysis reliability and efficiency (time required to conduct posture assessment), van Wyk et al. [2009] determined the 

ideal trade-off between the magnitude of classification error and the number of classification 

errors. An interface similar to that of 3DMatch 

[Callaghan et al. 2003] was used, which showed 

graphical representations of standardized posture categories in various views. The analyst 

decided which posture category most closely 

resembled (matched) the observed posture in 

the video frame depicted on the screen (Figure 

B1) and then selected that category by clicking 

Figure B1. Sample interface used for determining the optimal posture category size (adapted from van 

Wyk et al. [2009]). Posture categories of different size were presented below video images with segments 

at known angles.
16 | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement

on it with a mouse. The same video images were 

randomly shown to participants on the interface above five different sizes of posture categories, ranging from a 10° category size (more 

categories in the classification) up to a 75° category size (fewer categories in the classification). 

The number of posture classification errors, the 

magnitude of error, and the decision time taken to make each posture selection were recorded. The number of errors represented a simple 

count of how many errors were made. The magnitude of the error was the number of degrees 

difference between the middle of the measured 

(true) posture category and the middle of the 

category selected by the observer.

Two curves were plotted as a function of the size 

of the posture categories in the scale. One curve 

represented the magnitude of the errors and the 

other the number of errors. The point of intersection of the two curves was identified (Figure 

B2) for postures of the trunk (flexion/extension 

and lateral bend), shoulder (flexion/extension 

and abduction/adduction), and elbow (flexion/

extension). Selecting a posture category size 

larger than the intersection point resulted in 

fewer posture classification errors but a higher 

magnitude of classification error. Conversely, 

selecting a posture category smaller than the 

intersection point reduced the magnitude of 

error but significantly increased the number of 

posture misclassifications. An example of this is 

seen in Figure B2, in which the optimal posture 

category size for shoulder abduction was determined to be 30°. This information was used 

to establish the optimal posture category sizes 

shown in Figure 2. 

The proximity of a postural joint angle in relation to the posture category boundary also 

has a significant impact on the analyst’s ability 

to discriminate between adjacent posture categories. When an observed posture is closer to 

(that is, within 2°–4° of) a boundary between 

categories, decision time is increased by 7% and 

the posture is more likely to be classified incorrectly than when the posture is in the middle 

of the posture category [Andrews et al. 2008a, 

2008b; Weir et al. 2011]. Implicit in this type 

of posture classification system is a trade-off 

Figure B2. An example of the trade-off between magnitude of posture classification error and number of 

errors. The results depicted here are for shoulder abduction [van Wyk et al. 2009].
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 17

between decision-making time and accuracy of 

classification. In order to improve the probability of correct posture classification and reduce 

decision-making time, the posture categories 

need to be relatively large, with few boundaries. However, increasing the size of the posture 

category reduces the resolution with which a 

posture can be identified. This would impact 

the output of any biomechanical model that is 

used to predict joint or segment loads on the 

basis of the specific posture input [Andrews et 

al. 2008a].

Wrist postures of concern as risk factors for musculoskeletal disorders (MSDs) are non-neutral 

flexion/extension and radial/ulnar deviation. 

Posture of the forearm in pronation/supination 

(twist of the forearm about the long bones) is 

also observed in many job assessment methods. 

Optimal posture category sizes for the wrist 

and forearm have not been determined in the 

same experimental manner as those in Figure 

2 for the trunk, shoulder, and elbow. However, 

it is reasonable to believe that the selection of 

posture categories for the wrist and forearm 

can be guided by the same trade-off between 

the number of errors and the size of a resulting 

error in the posture classification. An important consideration is the smaller size of the hand 

and forearm, which are observed in estimating 

wrist joint posture, and the narrower range of 

motion of this joint. The range of motion of 

the wrist from full flexion to full extension is 

approximately 150°. However, in radial/ulnar 

deviation, the range of motion of the wrist is 

much smaller, with ulnar deviation accounting 

for only about 30° of available motion (radial 

deviation is approximately 20°). A larger number of posture classification errors should be expected if one attempts to classify wrist postures 

with a precision that is equal (in number of posture categories) to classifications of larger joints 

such as the elbow or shoulder [Lowe 2004b]. 

Visually discriminating among multiple levels 

of wrist radial/ulnar deviation categories is likely to result in even more errors than with larger 

joints because of the difficulty of the task. 

Assessments of MSD risk factors, including 

the assessment of posture, should have high 

internal and external validity [Kilbom 1994]. 

Internal validity refers to the degree of agreement between the observation-based measures 

of risk factors and reference standards for these 

risk factors. The studies described above have 

more firmly established expectations for internal validity, and it is largely this work on which 

the present emerging practice is based. External validity refers to how strongly the analysis 

method results predict risk of MSDs. Knowledge of the relationship between physical risk 

factors (including working posture) and MSD 

risk continues to be advanced through epidemiological studies of workplace injury prevalence 

and mechanistic studies of tissue response to 

physical loads. A complete review of evidence 

related to the relationship between physical risk 

factors and MSDs is beyond the scope of this 

document (for complete reviews see NIOSH 

[1997] and NRC/IOM [2001]). The approach 

described in Section 3 of this document is 

consistent with existing evidence related to 

the external validity of MSD risk prediction. 

For example, more generally, it is known that 

trunk posture affects biomechanical forces and 

moments about the lumbar spine and the activation of muscle tissue required to support and 

stabilize the spine in response to these external 

loads. Increasing exposure to flexion, lateral 

bend, and axial rotation of the spine increases 

risk for back injury. It is accepted that shoulder 

postures in which the arm is elevated create the 

potential for impingement in the subacromial 

and thoracic outlet spaces [Flatow et al. 1994] 

and place stresses on musculo-tendinous and 

joint capsule and ligament structures. Increasing arm elevation increases risk for impingement-related and rotator cuff injury. 

More specific posture categories that validly 

predict MSD risk across diverse work situations, and in combination with other risk 

factors, are difficult to establish. For example, there is some evidence that MSD outcomes may be more sensitive to non-neutral 
18 | Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement 

posture than could be detected in the present 

approach to categorizing posture. For example, an epidemiological study by Punnett et 

al. [1991] adopted an a priori neutral category of 0° to 20° trunk flexion and showed 

increased injury risk with trunk postures 

exceeding 20°. This was demonstrated by 

calculating an odds ratio for the likelihood 

of injury when trunk flexion posture is less 

than 20° versus greater than 20°. The method presented in this document categorizes 

0° to 30° as the neutral trunk flexion category, not because trunk postures less than 

30° are necessarily of no risk but, rather, because trunk flexion posture is more reliably 

classified by observation with the 30° range. 

An observation-based posture assessment 

method must consider both internal and external validity of the posture classification 

(measurement). 
Observation-Based Posture Assessment: Review of Current Practice and Recommendations for Improvement | 19

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