posture-based risk assessment for improvement of physical

Original Paper

Posture-Based Risk Assessment for Improvement of Physical Workload: Case Study for an Assembly Line

Masato TAKANOKURA

†1, Tomohiko TANAKA†1, Ichie WATANABE

†2, Munenori KAKEHI†3, Hiroshi

NOJIRI †4, Hiroyuki MURATA

†5, Hideaki UTSUKI †5, Atsushi UCHIDA

†5 and Masahiro NAKAMURA †6

Abstract: Ergonomic risk assessment is required to prevent work-related musculoskeletal disorders.

Some assessment techniques in a broad range of complexity have been suggested. A complex tool

quantifies a detailed risk, but it is time-consuming. In this study, a simple risk assessment method is

proposed to improve (Kaizen) production line work by lowering the physical load on the worker.

This method is based on Rapid Entire Body Assessment (REBA), but it can be used not only for

posture analysis, but also for consecutive repetition work in any production line. A station work unit

is divided into work elements. Two posture scores per work element are estimated for the upper-

limbs as well as for the head, trunk and legs, respectively. Physical workload on the work unit is

obtained from the weighted average of workloads on consecutive work elements applying a tact time.

The daily workload is also examined for the workload on work units and production planning. The

validity of the proposed method is investigated by an assembly line of vending machines for case

study.

Key words: safety, Kaizen, assembly work, production planning, workstation

1 INTRODUCTION

Risk assessment is required to prevent work-

related musculoskeletal disorders [1-3] and human

error [4]. Some physical workload arises from

assembly work in line production systems due to

poor body posture or heavy weight loads. The

physical workload on employees working in

assembly lines should be quantified. Quantification

of physical workload leads to relieving workload,

enhancing occupational safety, and improving worker

morale. In addition, enterprises could design

production lines with smaller workloads and estimate

workloads before constructing or improving

production lines. In other words, quantification of

physical workload can simultaneously achieve

occupational safety and line production system

productivity. This is one of the core topics in the

maintenance of factory facilities. Safety can be

established by assessing risks regarding human

factors and facility/product design.

Some postural analysis techniques have been

proposed for ergonomic risk assessment. For example,

OWAS has high generality, but it is a low sensitivity

method [5, 6]. On the other hand, the NIOSH equation

for manual lifting tasks is highly sensitive, but it has

low generality [7]. Previous analysis techniques can

estimate workload at any moment (momentary

workload). Workers are not engaged in simple work.

Their work is composed of several work elements,

although they are repetitive as daily duties.

Cumulative workload could not be estimated

sufficiently from the analysis techniques previously

proposed. Thus, there are some problems related to

risk assessment at assembly works. A complex

analysis technique can quantify detailed risk, but it is

time-consuming. It is also necessary to consider risk

assessment for consecutive repetitive work.

Some simulators have been developed and used to

construct and evaluate production lines [8, 9]. They

have high validity and effectiveness, but high skill and

knowledge are also required to extract the implicit

powers of such simulators. Enterprises have to assign

professional workers in the workplace or outsource the

analysis of production lines to others. Enterprises also

†1 Kanagawa University †2 Seikei University †3 Tokyo University of Science, Currently, Fukushima University†4 Applied Bridge †5 SANDEN CORPORATION †6 LEXER RESEARCH Inc. Received: March 30, 2015 Accepted: March 31, 2016

J Jpn Ind Manage Assoc 67, 338-347, 2017

338 J Jpn Ind Manage Assoc

decide on management strategies according to time

and cost. However, it takes a longer time for

simulators to improve productivity on assembly lines.

Simulators are very useful for manufacturing

improvement, but factory managers such as foremen

and industrial engineers also desire simpler risk

assessment methods for assembly works.

The aim of this study was to develop a risk

assessment method for foremen and/or industrial

engineers. It is possible to use this method without any

particular knowledge or skills and estimate workload

for consecutive repetitive work on an assembly line in

advance. Suzuki et al. [10] visualized functional

design processes during product development in a case

study on multi-product lines of vending machines.

They focused on the upper stream of the

manufacturing process. Our study was also a case

study on the same multi-production lines, but it mainly

addressed the lower stream of the process.

2 POSTURE-BASED ERGONOMIC RISK ASSESSMENT

2.1 Risk Assessment Sheet

The tool proposed for ergonomics risk assessment

was based on RULA and REBA [11, 12]. The former

is a rapid upper limb assessment. Postural scores are

estimated from upper limb segments, a neck and a

trunk. Load/Force and repetition scores are added to

them. The latter estimates scores in the same way, but

leg posture is included. However, RULA was

developed to estimate the workload for the upper

limbs, not for the entire body. Additionally, it is too

detailed for assembly work. Hignett and McAtamney

[12] reported REBA as a risk assessment tool for the

entire body, but it was a technical note. REBA is

based on RULA for the risk assessment of upper limbs,

a neck and a trunk. They used REBA to assess the

working posture of a physiotherapist involved in

treating a patient with hemiplegia [12]. REBA is

useful, but it should be modified to be applicable for

assembly work in a production system. In order to

assemble any part to a product, workers engage in

repetitive work. However, the assembly work cycle

depends on production planning. The enterprise

manufactures many products during high-season

demand, but it reduces the number of products during

low-season demand. The enterprise adjusts the

production speed and the content of assembly work

(e.g., number of workers, division of labor) according

to customer demand. This viewpoint is not included in

REBA. In addition, it has not been proven that

foremen and/or industrial engineers could use RULA

and REBA immediately and easily to improve

(Kaizen) actual work in an assembly line. Therefore, a

posture-based risk assessment method should be

developed for consecutive repetitive work in assembly

lines. Figure 1 shows the relationship between RULA,

REBA and the tool proposed.

Enomoto et al. [13-15] re-examined the workload

on legs and load score in the experiment. They

proposed a modified risk assessment method at any

moment using RULA, REBA, and the experiment

results. Load score was also graded using the

experimental results and the Japanese guideline [16].

The modified method is explained in the Appendix. It

is based on RULA and REBA, but the posture score

for legs and load score from the experiment were re-

examined. They demonstrated physical tasks in

standing, half-sitting, and sitting for the leg posture

score. They also demonstrated carrying tasks with

some weight loads for load score. Physical workload

was examined using a questionnaire and a subjective

rating.

We developed a spreadsheet using Microsoft Excel

for foremen and/or industrial engineers to use at

workplaces with assembly lines, as shown in Fig. 2.

The spreadsheet was based on the modified risk

assessment method proposed by Enomoto et al. [14,

15]. The left-hand side was an assessment for upper

limb segments (upper arms, forearms, and wrists). The

middle part was for the neck, trunk, and legs. The

Fig. 1. Relationship between proposed tool, RULA,

and REBA.

Vol.67 No.4E (2017) 339

STEP1 Position (Angle) of Upper Arm 1 ？ STEP4 Position (Angle) of Trunk 4 ? STEP6 Weight of Supported Load

? ?Trunk

STEP2 Position (Angle) of Forearm 1 ?

STEP5 Position (Angle) of Leg 4 ?

STEP3 Position (Angle) of Hand 1 ?

0

??

　

Very high risk

Neck（Head）

3 A. Score: Arm・Hand ＋ Load

9

11

C. 「Supported Load」

High risk

A. 「Arm」・「Hand」 B. 「Trunk」・「Neck」・「Leg」2

B.Score:「Trunk」「Neck」「Leg」

（Detailed Analysis for Upper Arm）

（Detailed Analysis for Leg）

　

（Detailed Analysis for Trunk and Neck）

9

Final Score (Entire Body)

71 A. Score:「Arm」「Hand」

（Detailed Analysis for Hand）

B. Score: Trunk・Neck・Leg＋ Load

FinalScore(線形)

-20°～+20° <-20° +20°～+45° +45°～ >+90°

Shoulder is elevated （tensed）

Upper arm is abducted （raised laterally）

Upper arm and/or elbow is supported

60°～100°0°～60° 100°～180°

Hand is twisted

NOT extended or flexedHand is extended or flexed

0kg～<10kg 10kg～<20kg 20kg and more

Trunk is twisted and/or bent laterally

-20°～20° >+20°<-20° Sitting on chair

Standing Half-Sitting Sitting

Upper arm is relaxed

Upper arn is twisted with elbow flexedNeck is extended （>+20゜; look-down）

Neck is twisted and/or bent laterally

Neck is flexed (look-up)

Not supported equally by both legs, Or one knee touches floor

（ >+15゜ or <-15゜）（ -15゜～+15゜）

Except（+150゜～+180゜） Contact: Thigh ad ShankNOT contact: Thigh and Shank

Fig. 2. Spreadsheet for modified risk assessment method at any moment.

upper-right-hand side was a load score. If radio but-

tons and check boxes were selected for any work, the final

score FS was displayed on the lower-right-hand side.

2.2 Workload Estimation on Work Element and Work Unit

Figure 3 shows how to assess a repetitive workload

in an assembly line. A work unit, which was one cycle

of an assembly task at a workstation, was divided into

some work elements [17]. WEi stands for the ith work

element and has a continuous elemental time ETi.

Foremen and/or industrial engineers could estimate the

final score FSi for the ith work element WEi as work-

load on a work element using the spreadsheet, as

shown in Fig. 2.

As shown in Fig. 3, if the ith work element WEi

with the final score FSi was assumed to be lasting for

elemental time ETi, the workload on a work unit WLc

could be estimated from the weighted average of FSi

on WEi for all work elements (i=1, …, n). The

operation time at a workstation was the sum of the

elemental times, but it depended on the workstation

and the content of assembly work. For example, the

operation time tended to be shorter for products with

fewer assembly parts or having a smaller size.

Therefore, the tact time (TT) was used to estimate the

workload on a work unit:

TT

ETFSWL

n

iii

C

1 . (1)

In this study, the tact time was defined as TT = daily

operation time/production number. This definition was

used in an assembly line for a case study.

Fig. 3. Schematic diagram of workload estimations on

work unit (one cycle) and elements WEi (i=1, …,n).

3 CASE STUDY

3.1 Daily Workload Estimation with Production Planning on an Assembly Line

As a case study, we estimated the workload in a

production line for vending machines. This production

system assembled various vending machines of

different sizes using the same line [10]. Their sizes

were classified into small (S), medium (M), and large

(L). The size (S, M, L) indicated how many kinds of

drinks were stored in the vending machine.

The concept of daily workload estimation is

illustrated in Fig. 4. An assembly line was operated by

production planning. The workers assembled some

parts of the vending machines with different sizes at a

workstation. The number of vending machines was NS


for small, NM for middle, and NL for large products.

The workload on a work unit was estimated using Eq.

(1) as WLCS for small, WLCM for middle, and WLCL for

large size. Thus, we could estimate the daily

workload using the following equation:

LCLMCMSCSD NWLNWLNWLWL . (2)

Equation (2) indicates a cumulative daily workload of

a worker. We could estimate this in advance if we had

production plans and the workloads for the work units

of the products to be assembled at the workstations.

Fig. 4. Daily workload estimation from workload on work unit and production plan.

3.2 Workload Estimation on Five Workstations

We should consider the validity of the method

proposed by carrying out assembly on the

workstations in the vending machine production line.

In this line, there were approximately 70 workstations

total for the main line and some sub-lines. We

addressed five workstations (WS): (A) assembling

door-base, (B) assembling sub-door parts, (C)

assembling the refrigeration unit, (D) assembling

beverage columns, and (E) inspection. These

workstations had a relatively high workload compared

to the others.

First, we divided a work unit of the five

workstations into work elements by observation of

assembly work. On the basis of the spreadsheet (Fig.

2), we measured the articular angles directly using a

protractor. The mass of the weight load was measured

using a weight scale. The final score of the ith work

element (FSi) could be obtained from the data

measured and recorded on the spreadsheet.

In addition, an elemental time (ETi) was measured

using development-type work analysis software (Time

Prism, Nihon Seikoh Giken, Japan). We took video

images during assembly work using a video recorder

(HX-DC3, Panasonic, Japan), and then they were

transferred to a personal computer (Inspiron 620s,

DELL). We identified the work element from the

frames captured and estimated the elemental time

(ETi) using Time Prism. The tact time was obtained

from production numbers and daily operation time on

the day surveyed.

4 RESULTS

Table 1 shows the data measured at WS (A):

assembling the door-base. The type of work element

was classified into normal time and allowance time.

The former included main and set-up activities. The

latter meant activities other than those done in the

normal time, such as waiting or unnecessary

movement. For example, in the work unit of “1. Carry

door frame,” the final score was 12. It was judged as

“very high risk” because the worker assumed a poor

body posture and carried a heavy part (door frame).

The elemental time was 8.1s, 9.4s, and 8.3s for the

small, medium, and large-sized machines, respectively.

Assembly work for the medium and large sizes were

composed of the same work elements. However, the

work for the small-sized machines was different from

them because of vending machine specifications. As a

result, the workload on this work unit was obtained as

3.71, 3.59, and 3.72 for the small, medium, and large-

sized machines, respectively. Figure 5 illustrates the

estimated daily workload at WS (A). As a case study,

the assembly line produced 60, 165, and 40 small,

medium, and large-sized machines on the day

surveyed. We were able to achieve a daily workload of

963.75.

Table 2 indicates the daily workload conducted at

the five workstations. There was just one worker at

WS (A)-(D), but four workers engaged in the

simultaneous inspection of vending machines at WS

(E) because the inspection task had many more work

elements than the other tasks. Therefore, the workload

was obtained as 1/4 of the workload estimated for one

worker using Eq. (1). The workload was the smallest

at WS (E) and the largest at WS (A) among the five

workstations.

Vol.67 No.4E (2017) 341

Table 1. Measured variables at workstation (A): assembling door-base.

Work element Type of

work element

Final

score

Elemental time [s]

Small Medium Large

1. Carry door frame Normal 12 8.1 9.4 8.3

2. Put packing seal on lower-side of upper window Normal 3 16.9 20.4 17.9

3. Put packing seal on upper-side of upper window Normal 4 13.4 19.9 21.7

4. Put packing seal on lower window Normal 4 26.8 28.9

5. Put packing seal around hinges of the door Normal 4 5.3 5.1 5.6

6. Put packing seal around control panel Normal 5 1.4 7.5

7. Tighten screws on panel frame Normal 3 27.4

8. Apply waterproof sheet Normal 3 13.2

9. Put packing seal around coin mechanism Normal 3 11.0 12.4 7.3

10. Rotate door-base stand Normal 3 4.6 3.7 4.1

11. Put packing seal on upper-side of door cover Normal 4 2.9 1.9

12. Put packing seal on lower-side of door cover Normal 3 3.3 4.9

13. Tighten screws on control panel Normal 4 7.6 9.3 10.4

14. Apply protecting tape Normal 3 14.5

15. Assemble panel frame Normal 3 15.8

16. Others (move, etc.) Allowance - 10.8 9.2 5.8

Fig. 5. Daily workload estimation at workstation (A):

assembling door-base. Table 2. Results of daily workload (WL) estimation at five workstations.

WS WL in one cycle Daily WL Small Medium Large

(A) 3.71 3.59 3.72 964 (B) 2.24 1.94 2.49 554 (C) 2.26 2.29 2.57 616 (D) 1.19 1.89 2.30 475 (E) 1.27* 1.67* 1.96* 430*

The * mark indicates that four workers were simultaneously engaged at the workstation. This WL was obtained as 1/4 of the WL observed using Eq. (1) for one worker.

5 DISUCSSION

5.1 Proposed Workload Improvement (Kaizen)

The proposed risk assessment method estimated the

workload at a work unit using Eq. (1). In some cases, the

workload was estimated as the same value between a

smaller load with a longer time and a larger load with a

shorter time. The Japanese guideline for prevention of

lower back pain [16] recommends that a worker does not

assume the same posture for long periods of time while

standing or sitting on a chair. The method proposed could

identify such risky work. The workstation with a higher

workload could be found using Eqs. (1) and (2). For this

case study, WS (A) (assembling door-base) had the

highest workload, as listed in Table 2, and it should be

improved first. Next, we could identify the high-risk work

element at this workstation by comparing final scores and

elemental times. As shown in Table 1, the work element

“1. Carry door frame” was the first priority to be improved

because it had the final score of 12. However, the work

element “4. Put packing seal on lower window” was the

second priority to be improved because it had a longer

elemental time with a final score of 4. The method

proposed could find risky work elements with the same

posture for longer periods of time using the final scores

and elemental times listed in Table 1 as well.


WS (A) (assembling door-base) had the highest

workload. As shown in Table 1, this workstation was

composed of 15 normal work elements. The highest

workload was the work element “1. Carry door frame.” It

was caused by a poor body posture and a heavy supported

load. The frame is a main part of the door-base, and its

weight is heavy. The worker carried a heavy frame, even if

only a short distance. At the same time, the worker leaned

his upper body forward with extended upper arms as he

put the heavy frame on the door-base stand. As the first

improvement, we could propose re-designing frames so

that the frame is lighter or separated. The workload could

also be reduced by two workers carrying a frame. This

improvement was validated at WS (C), as described later.

In addition, some power-assisted devices for workers such

as an exo-skeleton have been used in workplaces [18].

However, the elemental time of this work element was

about 10% of the operation time, as shown in Table 1.

Furthermore, the worker puts packing seals all over the

frame after carrying the door frame. Power-assisted

devices are also expensive, and the benefit is not much

higher than the cost. Thus, we considered that using

power-assisted devices for WS (A) is not a beneficial

option.

The second highest element was “6. Put packing seal

around control panel.” The control panel was located

below the worker’s chest. The higher workload was

caused by the leaning posture of the worker’s upper body.

We could reduce the workload by raising the door-base

stand or lowering the floor at the workstation. As another

measure, the worker sat on a chair during this work

element. These improvements reduced the workload at

WS (A).

There was one worker at WS (A)-(D), but four workers

engaged in parallel at WS (E) (inspection). This work

required a longer time than the others because there were

many work elements for precise inspection. Parallel work

with four workers reduced the workload for respective

workers; therefore, the workload was relatively low at WS

(E) compared to the others, as shown in Table 2. In

addition, female workers were engaged in inspection work

at WS (E) because the physical workload was lower than

assembly work, which requires greater muscular force to

carry heavy parts. The division of work at WS (E) was

reasonable for enhancing occupational safety.

We were able to find other ways to introduce

improvements (Kaizen) for other workstations. However,

a Kaizen activity had already been executed by industrial

engineers at WS (C) (assembling cooling unit). The

workers suffered from a higher workload due to carrying a

cooling unit for approximately 2 m from a workbench to a

vending machine at WS (C). A new specification for

vending machines was introduced, and some cooling units

became heavier at that time. The worker was not permitted

to carry a new cooling unit unassisted; two workers carried

a cooling unit together. This Kaizen activity was valid for

the actual workplace, but it was based on the experience of

industrial engineers. The risk assessment proposed

provides a quantitative tool and scientific improvements to

workplaces.

We could classify the assembly work into size-

independent and size-dependent tasks as shown in Table 2.

The former was WS (A), (B), and (C). Assembly work at

these workstations was comprised of respective work

elements, and it did not have simple repetitive elements.

However, the latter two tasks at WS (D) and (E) had

simple repetitive work elements; therefore, they were size-

dependent. For example, at WS (D) (assembling

beverage columns), the worker installed columns (storage

for cans or plastic bottles) into a vending machine, and the

number of columns depended on the size of the vending

machine. The larger machine had many columns.

Similarly, WS (E) (inspection) had simple repetitive work.

For example, a worker pushed buttons in turn and picked

up cans and plastic bottles from an outlet port below. The

repetitiveness determined the amount of physical

workload within a work element.

5.2 Comparison of Method Proposed and Other Risk Assessment Methods

David reviewed ergonomics methods for risk

assessment to prevent work-related musculoskeletal

disorders [19]. The method proposed was considered to

be one of the simpler observational methods, which

included OWAS, RULA, REBA, and the NIOSH equation.

We described a comparison of the RULA, REBA, and

method proposed in Section 2.1. The NIOSH equation is a

measurement of posture related to biomechanical load, and

can identify risk factors [19]. However, it is limited to

manual handling. The method proposed has higher

generality than the NIOSH equation because it is

comprised of assessing body posture and load support.

OWAS is a time sampling method for body postures and

analyzes the overall body posture. It includes the

Vol.67 No.4E (2017) 343

assessment of posture and load/force, but not movement

frequency [19]. In addition, OWAS does not include risk

assessment for hands and neck. Workers used their hands

for some assembly work such as installing packing seals,

as shown in Table 1. Neck movement is also included in

assembly work. For example, looking up or down to put

packing seal on an upper window or door cover. OWAS is

a useful method with high generality, but its accuracy is

not sufficient for assembly work. Movement frequency is

also included in RULA and REBA, but it is much simpler

to use for risk assessment of assembly work. Thus, the

method proposed had sufficient accuracy for risk

assessment of assembly work.

5.3 Limitation and Future Direction of Proposed Risk Assessment

The risk assessment method proposed evaluated

physical workload precisely. However, there were some

limitations to it. The first limitation is the trade-off

between accuracy and time/cost for the assessment of

physical workload. Previous analysis techniques paid

attention to workers’ posture at any moment. These

techniques are valuable, but they are lacking in accuracy

because assembly work is comprised of several work

elements. However, the work element can be divided into

motion units and then into motion elements [17]. Even

during a work element, workers change their posture

consecutively. The method proposed can evaluate the

highest workload within varied postures of a work element.

We could evaluate a more accurate workload using motion

units or elements instead of work elements, but it forces

foremen and/or industrial engineers to take a longer time.

As a result, enterprise costs will rise. The method

proposed has an appropriate accuracy for risk assessment.

The second limitation is workload estimation for

maintaining the same working posture for a longer time. In

this study, we estimated the workload on a work unit WLC

as the weighted average of the final score FSi for the ith

work element WEi (Eq. (1)). However, the Japanese

guideline for prevention of lower back pain [16] points out

that the same working posture for long periods of time

enhances a risk of lower back pain. As shown in Table 1,

the work elements “4” and “7” had the final scores of 4

and 3 for approximately 30 seconds, respectively, at

workstation (A). These work elements would have a

higher workload affecting lower back pain due to a longer

elemental time. OWAS could be used as reference data for

any workload because the data are obtained from time

sampling for body postures. RULA and REBA do not

include elemental times of work elements. The method

proposed estimated the workload from the weighted

average of FSi for WEi. It has the advantage of accuracy

for risk assessment, but we should discuss the validity of

the method further via case studies; especially for the risk

of the same working posture for a longer time and for

application to other assembly lines.

The third limitation is the laterality of working posture.

The workers did not assume a symmetric posture during

their tasks. For example, at WS (E) (inspection), the

workers pushed buttons in turn by raising his/her one arm

upward and picked up cans or plastic bottles from the

outlet port below with downward motion of the arm.

Similar asymmetry was observed at other workstations.

We could evaluate workload more precisely by measuring

working posture bilaterally, but this is also a trade-off

between accuracy and time/cost. Furthermore, another

problem is determining which side of the body is dominant.

Even if a worker assumes a poor posture of any body part,

physical workload was not high when not being used for

assembly work. In this study, physical workload was

estimated for the dominant side of the body during

assembly work.

This study proposes a simple risk assessment method

for assembly work managed by foremen and/or industrial

engineers, but some problems remain unsolved. We should

investigate the relation between predicted workloads and

subjective feelings of workers. As already described,

female workers were engaged in inspection work at WS

(E) because they inspected the assembled products

precisely. Another reason was the need for less muscular

force for inspection work. These decisions were

reasonable, but we should consider gender differences and

the appropriate arrangement of workers in a quantitative

manner using the method proposed. Some improvements

were proposed for reducing physical workload, but their

effectiveness should be examined by Kaizen activities in

actual workplaces. In addition, we should reduce physical

workload not only by Kaizen activities, but also by

redesigning products or facilities in the line production

system. In particular, we could achieve occupational

safety and productivity of line production systems

simultaneously by incorporating design information

of assembled parts and physical workload within


assembly work, like the bill of materials (BOM) for

design or manufacturing [10].

6 CONCLUSION

A simple risk assessment method was proposed for

foremen and/or industrial engineers managing line

production systems with assembly work. Physical

workloads at workstations could be evaluated using a

spreadsheet on Microsoft Excel. Workload on a work

unit was estimated by the weighted average of

workloads on work elements with elemental times. As

a case study, we estimated daily workloads at five

workstations in an assembly line of vending machines

with production planning. We also proposed some

Kaizen of assembly work with higher workloads. The

risk assessment method proposed was an appropriate

method for foremen and/or industrial engineers in

terms of the trade-off between accuracy and time/cost.

ACKNOWLEDGMENTS

This work was partially supported by JSPS

KAKENHI Grand Number JP26282088. We would

like to express our appreciation to Prof. Kinya Tamaki

of Aoyama Gakuin University for his valuable advice

and support. We are also grateful to SANDEN

CORPORATION for their cooperative support.

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APPENDIX

The spreadsheet for risk assessment was based on RULA,

REBA, and the experimental results [13-15]. Load score

was also graded using the experimental results and

Japanese guidelines [16]. The sheet is shown in Fig. 2,

but we estimated risk scores regarding body posture and

load supported applying Tables A1-A4. Respective risks

were evaluated from matrices in Figs. A1 and A2. We

could obtain the final score from the matrix as shown in

Fig. A3. Recommendations were determined from final

scores listed in Table A5.

Table A1. Assessment for upper arm

Step 1: Position (Angle) of Upper Arm

-20° (flex) ↔ +20° (extend) 1 pt

< -20° (flex) 2 pt

+20° ↔ +45° (extend) 2 pt

+45°↔ + 90° (extend) 3 pt

> +90° (extend) 4 pt

Detailed Analysis for Upper Arm

Shoulder is elevated (tensed) +1 pt

Upper arm is abducted (raised laterally) +1 pt

Upper arm is twisted with elbow flexed +1 pt

Upper arm and/or elbow is supported -1 pt

Upper arm is relaxed -1 pt

Table A2. Assessment for forearm and hand

Step 2: Position (Angle) of Forearm

0° ↔ +60° (flex) 2 pt

60° ↔ +100° (flex) 1 pt

100° ↔ +180° (flex) 2 pt

Step 3: Position (Angle) of Hand

Extended or flexed (>+15° or <-15°) 2 pt

Not extended or flexed (>+15°↔ <-15°) 1 pt

Detailed Analysis for Upper Arm

Hand is twisted 1pt

Table A3. Assessment for trunk, neck, and legs

Step 4: Position (Angle) of Trunk < -20° (extend) 2 pt -20° (extend) ↔ +20° (flex) 1 pt >+20° (flex) 2 pt Sitting on chair 1 pt

Detailed Analysis for Trunk and Neck Trunk is twisted and/or bent laterally +1 pt Neck is flexed (look-up) +1 pt Neck is extended (>+20°; look-down) +1 pt Neck is twisted and/or bent laterally +1 pt

Step 5: Position (Angle) of Leg Standing (150° ↔ +180°) 1 pt Half-sitting (no contact: thigh and shank) 3 pt Sitting (contact: thigh and shank) 2 pt

Detailed Analysis for Leg Not supported equally by both legs, or one knee touches floor

+1 pt


Table A4. Assessment for load supported

Step 6: Weight of Load Supported 0kg ↔ <10kg 0 pt 10kg ↔ <20kg 1 pt >20kg 2 pt

Table A5. Recommendation from final score (Fig. A3).

Final Score Recommendation

1 Negligible risk

2-3 Low risk

4-7 Middle risk

8-10 High risk

11-12 Very high risk

STEP2 Forearm

1 2 3 1 2 3 STEP3 Hand

1 1 2 3 2 3 42 2 3 4 3 4 53 3 4 5 4 5 64 4 5 6 5 6 75 5 6 7 6 7 86 6 7 8 7 8 97 7 8 9 8 9 9

Upper Arm

STEP1

1 2

Fig. A1. Evaluation matrix for arm and hand

1 2 3 4 5 STEP4 Trunk

1 1 2 3 4 52 2 3 4 5 63 3 4 5 6 74 4 5 6 7 8

STEP5

Leg Fig. A2. Evaluation matrix for trunk, neck, and legs

1 2 3 4 5 6 7 8 9 101 1 1 2 3 4 6 7 8 9 102 1 2 3 4 4 6 7 8 9 103 1 2 3 4 4 6 7 8 9 104 2 3 3 4 5 7 8 9 10 115 3 4 4 5 6 8 9 10 10 116 3 4 5 6 7 8 9 10 10 117 4 5 6 7 8 9 9 10 11 118 5 6 7 8 8 9 10 10 11 129 6 6 7 8 9 10 10 10 11 1210 7 7 8 9 9 10 11 11 12 1211 7 7 8 9 9 10 11 11 12 12

Step1-3:Arm,Hand

Step 4,5: Trunk, Leg

Fig. A3. Final score from two matrices (Figs. A1/A2)

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posture-based risk assessment for improvement of physical

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