ABSTRACT

This paper discusses the application of digital human 

models (DHM) to examine computer generated forces 

necessary to move specific joysticks by using roof bolter 

virtual operators to predict the forces experienced on the 

operator’s upper extremities. Using DHM and 

simulations of static movements, investigators analyzed 

predictions of joint moment and joint force effects on 

virtual operator’s right wrist, elbow and shoulder and 

compared them to different body dimensions and work 

postures. This study exemplifies the ease of estimating 

upper extremity loads on equipment operators using 

virtual operators and computer models of equipment and 

work environment. As expected, comparing results of 

percentage of joint force and moment reduction using 

the electronic control and of the mechanical control 

showed that the electronic control had lower joint forces 

and joint moments over the mechanical control. The 

average predicted value of the joint moment on the wrist 

was 1.84 Nm, elbow 7.85 Nm and shoulder 14.55 Nm 

and of the joint force on the wrist was 5.06 N, elbow 4.56 

N, and shoulder 3.67 N. Because the data illustrates 

low-level forces to move a joystick, inadvertent actuation 

of the control is addressed. Regardless of the findings, 

research is still needed on joysticks in real world 

situations such as an epidemiological assessment of 

equipment operators in the field before final 

recommendations and conclusions can be drawn.

INTRODUCTION 1

Manufacturers make design modifications to their 

equipment to improve the safety and efficiency of 

controlling it. Such is the case of a specific 

manufacturer of underground mining equipment that 

offers a mechanical or electronic joystick to control an 

appendage on a machine. Understanding the full impact 

that a design modification makes requires the analysis of 

 

1

The findings and conclusions in this report are those of the authors 

and do not necessarily represent the views of the National Institute for 

Occupational Safety and Health. 

data from laboratory and field experiments. Data 

collection from laboratory experiments could include 

data from test subjects operating mockup equipment or 

data generated from simulations that contain models of 

digital humans, machines and work environment. Field 

data collection takes place at the actual workplace such 

as in underground coal mine with actual equipment, 

operators and mine environment.

This paper exemplifies how using a DHM application as 

a research tool could help investigators begin to 

examine the impact of equipment modifications. The 

scope of this paper was not to provide conclusive 

evidence or results of impact and benefits between 

joystick controls, but to illustrate the application and 

expected predictions and results from DHM. The 

authors’ opinion is that more data and analyses are 

needed to fully understand any impact of machine 

modifications especially in the case presented in this 

paper. Additional research could include but not be 

limited to the following: an epidemiological assessment 

of modified equipment operators in the field and to 

determine whether or not laboratory findings correlate 

with what machine operators are experiencing in the 

field. This paper documents the investigation of the 

authors to examine computer generated forces 

necessary to move an electronic and mechanical 

joystick by using roof bolter virtual operators to predict 

joint forces and joint moments on the operator’s upper 

extremities. Researchers analyzed a database of 

simulation predictions of joint forces and joint moments 

on the right wrist, elbow and shoulder from combinations 

of work postures and operators with different body 

dimensions.

Changing from mechanical to an electronic based 

control has one prime advantage; it makes the device 

easier to move, by reducing the force and energy 

necessary for the operator to accomplish repetitive 

tasks. In this study the electronic control joystick was a 

seven inch long lever one inch in diameter that is 

connected remotely to a hydraulic system through 

electronic circuits and suitable transducers and 

 2007-01-2497 

Upper Extremity Joint Moment and Force Predictions when 

using a Joystick Control 

Dean H. Ambrose

National Institute for Occupational Safety & Health, Pittsburgh Research Laboratory 

Tim D. Burgess and Dave P. Cooper 

J.H. Fletcher & Co. 
actuators. The mechanical joystick was a two inch 

diameter knob attached to a twelve inch long lever that 

is connected directly to the hydraulic system. Both 

joysticks where mounted on a roof bolter appendage at 

the same height from the floor (see figure 1). Located 

on this appendage is a tool that the operator, using the 

joystick, would select a function to control its rotation 

and feed (vertical direction). 

Figure 1 – Electronic and mechanical control 

Joystick 

When selecting a function through the joystick, a force 

on the control must be applied to move a joystick to 

overcome the spring force used to center the joystick.

Once resistive force is overcome, the joystick moves. 

By continuing to apply a force on the joystick, the 

joystick is kept in position thus allowing the selected 

function to continue operation until released. When the 

joystick is released, a spring-loaded device returns the 

joystick to neutral position automatically.

Experiments in other industries have used computer 

models to examine force and moment effects on the 

wrist, elbow and shoulder. Oliver et al. (2000) used wrist 

flexion/extension models of Armstrong/Chaffin to 

determine if joystick controller use in off-road machines 

could contribute to hand and wrist repetitive strain 

injuries such as carpal tunnel syndrome (CTS). The 

investigation revealed that both the external fingertip and 

predicted internal wrist forces resulting from the use of 

the joystick were very low; indicating that CTS risk 

associated with this factor was slight. However, force 

forward, left/right side motions and all-left side motions 

were exerted by other portions of the fingers and hand, 

thereby under-predicting the tendon tension, internal 

wrist forces and the wrist angles. These observed 

forces were highest for motions that moved the joysticks 

to the sides rather than front to back. Consequently, the 

right and left motions for both hands posed a higher risk 

for CTS concerns.

Lindbeck et al. (1997) studied the net load and the force 

distribution in the shoulder during sanding of a ceiling. A 

biomechanical model predicted the load pattern in the 

shoulder from measured external forces and body 

postures. The study showed the proposed method can 

be used to evaluate loads and force distribution in the 

shoulder. Moreover, observable individual variations in 

work technique may explain why some workers develop 

musculoskeletal disorder while others do not.

Other experiments to determine forces on upper 

extremities include Niemeyer et al. (2004) and Van 

Drongelen et al.(2005) examining wheelchair-propulsion 

efforts on the shoulder and Shimada et al. (2001) on the 

wrists; Safran et al. (2005) studying sports activities that 

impart a stress on the elbow; and Hatfield (2003) using a 

combination of computer tomography, new reverseengineering software, CAD/CAM solid tools, and 

computer animations to study the human wrist 

METHOD

SUBJECT 

Three virtual male subjects, representing different body 

dimensions having measurements that populate 5th

-,

50th-, and 95th-percentile, performed motions associated 

with joystick that controls a roof bolter boom arm. Table 

1 provides information on height and weight of the virtual 

subjects.

EXPERIMENTAL DESIGN 

The use of virtual human models to analyze workplace 

hazards and improve workplace design is becoming 

more prevalent among human factors and ergonomics 

professionals (Badler et al., 2002; Chaffin, 2002; 

Ferguson and Marras, 2005). These virtual human 

models can be driven by human motion analysis 

techniques, providing the means by which humanmachine interactions can be analyzed. Investigators 

used UGS-PLM Corporation’s Jack virtual model and 

simulation software and its Loads and Weights module 

to generate and collect data on each virtual operator. 

During data collection, the virtual operator mimicked the 

operator’s right hand controlling the machine functions 

through a joystick control mechanism. Researchers 

evaluated a database of predictions of joint moment and 

force effects on the right wrist, elbow and shoulder. Joint 

moment predictions include the total force of moment 

that consists of three individual torques (x, y, x) about 

the joint axis. Joint force predictions include the total 

force that is comprised of compression and shear forces.

Researchers used only total moment and force values 

rather then their components, because of the level of the 

planned investigation. Also, for comparison purposes 

researchers post-processed the data to calculate 

percentage of force reduction between joystick controls. 

Further analysis used mechanical control’s resultant 

forces of joint moment and force predictions on each 

operator percentile and work posture and their 

combinations to compare the significance of the force 

reductions on each body joint. 

Table 1. – Virtual test subject anthropometric data 

subject height - cm (in) weight – kg (lbs) 

operator-05 164.6 (64.8) 67.1 (135.8) 

operator-50 175.5 (69.1) 84.6 (171.3) 

operator-95 186.9 (73.6) 106.8 (216.2) 
 Researchers tested virtual subjects in a representative 

work posture with respect to the boom assembly model 

and posed the right hand on the joystick as the operator 

would on the job. The independent variables consisted 

of three virtual subjects in three work postures. Two 

work postures represented an underground coal mine 

seam height requiring kneeling and the third represented 

working in a standing posture for higher seams. In these 

tests, researchers manipulated work postures and the 

six different pull and push directions required to move 

the joystick, see Table 2. For ease of identifying joystick 

direction, numbers were assigned for each test direction. 

Also, Fletcher (roof bolter manufacturer) measured in all 

directions the force to move the actual joysticks. The 

electronic joystick force averaged 15.6 N (3.5 lbf) and 

66.7 N (15 lbf) for the mechanical joystick. These 

measurements were used to simulate the operator’s 

forces exerted on the joystick. 

TEST PROCEDURE 

Researchers placed each virtual operator in a specified 

posture with respect to the boom assembly within the 

virtual computer environment as illustrated in Table 3.

Specific distances for each work posture were 

maintained between the boom arm and operator to 

accommodate the work space needed for each operator 

to mimic the pose for operating the joystick. The 

distance was measured from two reference points, one 

on the boom arm and the other the virtual operator’s 

center-of-mass site. Table 3 lists the distance from the 

boom arm for each subject and work posture 

combination and, shows the two reference points.

Ambrose et al. (2005) validated the same reference 

points and postures used in a previous study regarding 

roof bolter operators. The distances and postures are 

within the range to perform bolting tasks. The distances 

and postures varied slightly as using a joystick limits 

placement for potential right arm motion and back 

posturing, so allowing each virtual operator to handle the 

joystick naturally.

A joystick represents a rigid object that moves 

perpendicular about a fixed point in six different 

directions according to Table 2. These directions also 

helped to locate the force direction at the palm center by 

aligning the exerted force with both the joystick lever and 

perpendicular to the joystick top. The study by Oliver et 

al. (2000) suggests forces on joysticks are exerted by 

various portions of the hand (fingers and palm); 

therefore, this study used the palm center of the hand 

holding the joystick as the start point for load and 

direction. So for both exerted force values, researchers 

Table 2 – Relationship of the joystick test direction and function 

Function 

Direction number and description 

Rotation Reverse Rotation Feed Up Feed Down

1 – Push to the left 45° R

2 – Push straight forward R FUp 

3 – Push to the right 45° FUp 

4 – Pull to the right 45° RR

5 – Pull straight backward RR FDn 

6 – Pull to the left 45° FDn 

Table 3. – Distance (cm – (in)) between boom arm and virtual operator 

Work posture 

Virtual operator-(percentile) Both knees Right knee Standing

Operator-05 66.8 (26.3) 74.2 (29.2) 78.2 (30.8) 

Operator-50 70.1 (27.6) 74.2 (29.2) 83.1 (32.7) 

Operator-95 73.9 (29.1) 76.7 (30.2) 89.9 (35.4) 
collected joint moments and joint forces of the wrist, 

elbow and shoulder from all six test directions for each 

operator percentile/work posture combination. Also, 

operator percentile/work posture combination tests were 

carried out only once since static data was collected. 

Static tests started from direction 1 (push-to-the-left-45°) 

and to simulate other push or pull directions, continued 

clockwise to test directions 2, 3, 4, 5 and finally 6. 

Test Limitations

Jack’s Loads and Weights module does not have 

dynamic testing capability. If the module had this 

capability, tests would have mimicked operator motions 

in actual working conditions. These include varying test 

directions to reflect joystick functions typical of a bolting 

cycle and incorporating the joystick displacement from 

the neutral position before proceeding on to the next test 

direction. In addition, dynamic testing would give the 

ability to generate data on the full-range of hand motions 

over time prevalent to each joystick control type. Thus, 

the authors believe that dynamic testing would be more 

representative of tests conducted in laboratory and field 

conditions.

DATA ANALYSIS

Jack software bases the Loads and Weights module on 

a biomechanical model incorporating anatomical and 

physiological data from scientific literature, most notably 

from research of Grooso et al. [1987, 1988], Raschke 

[1994, 1996] and Chaffin [1997, 2000]. The module 

enabled investigators to specify how large a load is, 

place the load anywhere on the body, and adjust the 

direction it acts (load vector).

Researchers generated from Jack software a database 

of predictions with respect to the joint moment and joint 

force required to move a joystick. The results reflected 

the joint moment and joint force on the right wrist, elbow 

and shoulder for combinations of three operator 

percentiles (5th –, 50th – or 95th –percentile) and work 

postures (standing, kneeling on both knees and the right 

knee). Researchers post-processed the database and 

developed Tables 4, 5 and 6 showing percentage of joint 

moment and joint force reduction for the electronic 

control and the resultant joint moment and joint force of 

the mechanical control.

RESULTS

Data in Tables 4, 5 and 6 shows the predicted range of 

percentage of reduction using the electronic control and 

the resultant when using the mechanical control.

Percent reduction and resultant range for the joint 

moment on the wrist was 52.8% (0.3 Nm) to 92.7% (1.1 

Nm), elbow 45.7% (8.2 Nm) to 93.3% (10.1 Nm), and 

shoulder 46.5% (20.4 Nm) to 84.9% (19.1 Nm). Percent 

reduction and resultant range for the joint force on the 

wrist was 75.3% (6.8 N) to 76.2% (6.85 N), elbow 63.1% 

(7 N) to 68.6% (7.2 N), and shoulder 39.6% (7.8 N) to 

52.5% (8.1 N). The average predicted reduction of joint 

moments and joint forces on the wrist were 1.84 Nm and 

5.06 N, elbow 7.85 Nm and 4.56 N, and shoulder 14.55 

Nm and 3.67 N, respectfully. Further examination used 

mechanical control’s resultant forces of joint moment 

and force predictions on each operator percentile and 

work posture and their combinations to compare the 

significance of the force reductions on each body joint. 

Joint Moment Predictions – Results showed the right 

knee work posture with the highest reduction average of 

2.02 Nm for the wrist, 8.3 Nm for the elbow in the both 

knee posture and 15.4 Nm for the shoulder in the 

standing posture. Also, the results showed that the 95thpercentitle operator with the highest reduction average 

for the wrist (2.12 Nm), elbow (8.59 Nm) and shoulder 

(16.34 Nm). No one combination of work posture and 

operator percentile showed the highest reduction 

average for all three joints except standing work posture 

– 95th-percentile operator showed two with the highest 

reduction average: 8.59 Nm for the elbow and 16.34 Nm 

for the shoulder. The combination standing work posture 

– 5th-percentile operator showed the lowest reduction 

average for wrist (1.61 Nm), elbow (6.74 Nm) and 

shoulder (12.94 Nm). 

Joint Force Predictions – Results showed the both 

knee work posture with the highest reduction average for 

the wrist (5.17 N), elbow (4.78 N) and shoulder (4.23 N) 

and standing work posture with the lowest reduction 

average for the wrist (4.85 N), elbow (4.25 N) and 

shoulder (3.08 N). Joint force data shows the 95thpercentile operator with the highest reduction average 

for the wrist (5.15 N), elbow (4.66 N) and shoulder (3.96 

N) and the 50th-percentile operator with the lowest 

reduction average for the wrist (4.87 N), elbow (4.43 N) 

and the 5th-percentile operator’s shoulder (3.5 N). No 

one combination of work posture and operator percentile 

showed the highest reduction for the wrist, elbow and 

shoulder.

DISCUSSION

For operating a joystick on an underground roof-bolting 

machine, results showed that electronic control has a 

range of lower joint force and joint moment over the 

mechanical control. The 95th-percentitle operator and 

both knee work posture shows the greatest reduction in 

both joint moment and joint force. Because the results 

were very small values, the joint force variability with the 

change on joystick test direction was minimal. The 

average predicted joint moments (1.84 Nm) and joint 

forces (5.06 N) on the wrist were quite low (with a 

maximum resistive force of 66.7 N). In comparison, the 

joint force closely agrees with the average prediction 

results 6 N (with a maximum resistive force of 80 N) by 

Oliver’s et al. (2000) joystick study. No other joystick 

studies could be found to date to compare wrist joint 

moments, elbow and shoulder predictions. This could 

pose difficulty for future research in validating results on 

joystick controls using predictions from DHM. 
The data generated from simulations took three weeks 

to collect. This short time span illustrates how quickly 

data can be generated using DHM. As expected, the 

data showed low joint moments and joint force 

predictions. Kong et al. (2006) suggest models 

estimating low joint forces combined with minimal wrist 

dynamics are not a major factor in work-related 

musculoskeletal disorder. No such conclusion can be 

derived from the relative low joint force predictions using 

DHM in our study; however, the data did reveal lower 

joint moment and joint forces when operating an 

electronic joystick compared to a mechanical joystick 

control. Despite the findings the authors suggest 

additional research is needed in real world situations 

such as an epidemiological assessment of equipment 

operators in the field that have used both joystick 

controls. For example, even with electronically 

controlled joystick’s physiological benefits, it doesn’t 

provide as much feedback as those mechanically 

controlled. Some operators may prefer or even need, 

the “feel” of the mechanical joystick feedback to safely 

and efficiently handle the machine. Additional research 

that emphasizes psychosocial factors will also answer 

the questions do mine workers prefer or experience less 

comfort of one joystick over the other and have they 

found it to be less taxing by the end of their shift.

Furthermore, additional predictions from simulations 

using dynamic tests on realistic virtual operators 

controlling with their right and left hand can be used to 

help overcome shortcomings in the outcomes of this 

initial study. This can be realized if (1) the Loads and 

Weigh module would be expanded to include dynamic 

testing and (2) testing used realistic anthropometry of 

virtual test subjects of experience mine workers such as 

those used by Ambrose et al. (2005). 

Because small forces are evident to move the joystick 

electronic or mechanical controls, this drew attention to 

the authors regarding concerns of inadvertent actuation 

of the control. Consequently, Fletcher has addressed 

this potential problem by designing an electronic control 

joystick with an “enable” button molded in the knob that 

is easily depressed with no change of grip. The joystick 

still moves but no machine functions happen unless the 

button is depressed. 

The mechanical joystick control spring force is higher to 

keep the spool in the hydraulic control valve centered. 

Fletcher is investigating lowering this centering spring 

force but several problems must be overcome. The first 

is inadvertent actuation. Fletcher has developed an 

electrical button built into the mechanical joystick head 

to provide the enable button. Moving the joystick would 

not make a machine function happen unless the enable 

button was depressed. The second potential problem 

resulting from reduction in the hydraulic valve spring 

centering force is the possible loss of functionality in the 

valve. The valve design will have to change to 

overcome this problem.

CONCLUSION

This study illustrates the use of digital human models 

and simulations for estimating loads for equipment 

operators. This study showed, in the case of one 

specific manufacturer, that smaller joint moments and 

joint forces are apparent to operate an electronic control 

over a mechanical control. However, smaller actuating 

forces may result in inadvertent actuation of the control.

A solution to this potential problem is an “enable” button 

deigned into the joystick knob that, when depressed, 

allows functionality. In this study the average predicted 

reduction of joint moment on the wrist was 1.84 Nm, 

elbow 7.85 Nm and shoulder 14.55 Nm and joint force 

on the wrist was 5.06 N, elbow 4.56 N, and shoulder 

3.67 N. This information along with suggested future 

studies regarding epidemiological assessment of bolter 

operators in the field and additional dynamic tests on 

more realistic virtual operators could have an impact on 

the use of joystick controls. It is cautioned that additional 

laboratory and field studies are required before final 

recommendations and conclusions can be made. Note: 

The findings and conclusions in this report are those of 

the authors and do not represent the views of the 

National Institute for Occupational Safety and Health.

REFERENCES

1. Ambrose DH, Bartels, Kwitowski A, Helinski RF, 

Gallagher S, McWilliams LJ, Battenhouse TR [2005].

Mining roof bolting machine safety: A study of the 

drill boom vertical velocity. Pittsburgh, PA: 

Department of Health and Human Services, CDC, 

NIOSH, IC 9477, NIOSH publication No. 2005-128, 

56 p., 2005

2. Badler NI, Erignac, CA, and Liu, Y, 2002, Virtual 

humans for validating maintenance procedures, 

Communications of the ACM, Vol. 45, Issue 7, July 

2002, ISSN:0001-0782, pp. 56-63

3. Chaffin DB, Violante F, Armstrong T, and Kilbom A 

[2000]. Biomechanical Models in High Exertion 

Manual Jobs. Occupational Ergonomics Work 

Related Musculoskeletal Disorders of the Upper 

Limb and Back, Chapter 13, pp.157-177, 2000

4. Chaffin DB [1997]. Development of Computerized 

Human Static Strength Simulation Model for Job 

Design, Human Factors and Ergonomics in 

Manufacturing, 7(4), pp. 305-322, 1997

5. Chaffin DB, 2002, On simulating human research 

motions for ergonomics analyses, Human Factors 

and Ergonomics in Manufacturing, Vol. 12, Issue 3, 

pp. 235-247. 

6. Ferguson, SA, and Marras, WS, 2005, Workplace 

design guidelines for asymptomatic vs. low-backinjured workers, Applied Ergonomics 36 (2005), pp. 

85-95.

7. Grooso MR, Gonda RS, and Badler NI [1988]. An 

Anthropometric Database For Computer Graphics 

Human Figures, Proceedings of the 13th Annual 

Northeast Bio-engineering Conference. University 

of Pennsylvania, pp. 628-631, March 1988 
8. Grosso MR, and Gonda RS [1988]. Anthropometry 

For Computer Graphics Human Figures, technical 

report, University of Pennsylvania, 1988 

9. Hatfield E [2003]. 3-D wrist watching. Design

Engineer (Toronto), ISSN- 0011-9342, Volume 49, 

Number 7, pp. 20-21, August/September 2003 

10. Kong L, Jang H and Freivalds A [2006]. Wrist and 

tendon dynamics as contributory risk factors in workrelated musculoskeletal disorders. Human Factors 

and Ergonomics in Manufacturing, DOI: 

10.1002/hfm.20043, Volume 16 (1), pp. 83-105, 

2006

11. Lindbeck, L, Karlsson, D, Kihlberg, S, Kjellberg, K, 

Rabenius, K, Stenlund, B, and Tollqvist, J [1997]. A 

method to determine joint moments and force 

distributions in the shoulders during ceiling work - a 

study on house painters. Clinical Biomechanics,

ISSN- 0268-0033, Volume 12, Number 7-8, pp. 452-

460, January 1997 

12. NRC [1999]. Steering Committee for the Workshop 

on Work-Related Musculoskeletal Injuries, 

Committee on Human Factors, International 

Standard Book Number 0-309-06397-3, National 

Academy Press, Washington, DC. 

13. Niemeyer, LO, Aronow, HU, Kasman, GS [2004]. A 

pilot study to investigate shoulder muscle fatigue 

during a sustained isometric wheelchair-propulsion 

effort using surface EMG. American Journal of 

Occupational Therapy, ISSN- 0272-9490, Volume 

58, Number 58, pp. 587-593, Sep-Oct 2004 

14. Oliver, M, Biden, E, Rickards, J [2000]. Off-road 

machine controls: Investigating the risk of carpal 

tunnel syndrome. Ergonomics, ISSN- 0014-0139, 

Volume 43, Number 1, pp. 1887-1903, November 

2000

15. Raschke, U [1994] Lumbar muscle activity 

precdiction during dynamic sagittal Plane lifting 

conditions: Physiological and bio-mechanical 

modeling considerations. PHD Dissertation (Biomechanical Engineering), University of Michigan 

16. Raschke, U, Martin, BJ and Chaffin, DB [1996] 

Distributed moment histogram: A neurophysiologybased method of agonist and antagonist trunk 

muscle activity prediction. Journal of Biomechanics,

Volume 29 (12), pp. 1587-1596, 1996 

17. Safran, M, Ahmad, CS, Elattrache, NS [2005]. Ulnar 

collateral ligament of the elbow-EMB. Arthroscopy

- Journal of Arthroscopic and Related Surgery,

ISSN-0749-8063, pp. 1381-1395, June 2005 

18. Shimada, SD, Cooper, RA, Boninger, ML, Koontz, 

AM, and Corfman, TA [2001]. Comparison of three 

different models to represent the wrist during 

wheelchair propulsion. Trans Neural System 

Rehabil Eng, ISSN- 1534-4320, Volume 9, Number 

3, pp. 274-282, September 2001 

19. Van Drongelen, S, Van der Woude, LH, Janssen, 

TW, Angenot, EL, Chadwick, EK, and Veeger, DH 

[2005]. Glenohumeral contact forces and muscle 

forces evaluated in wheelchair-related activities of 

daily living in able-bodied subjects versus subjects 

with paraplegia and tetraplegia. Arch Phys Med 

Rehabil, ISSN- 0003-9993, Volume 86, Number 7, 

pp. 1434-1440, July 2005

Table 4 –Percentage of joint moment and force reduction using the electronic control and resultant of the mechanical 

control comparing both knees work posture and operator percentile versus joystick direction and body joint 

Operator percentile for both knees 

05 50 95

Joint

moment Joint force Joint

moment Joint force Joint

moment Joystick Joint force 

direction

Body 

joint % Nm % N % Nm % N % Nm % N

wrist 74.3 2.0 76.1 6.8 77.0 1.7 76.1 6.8 72.4 1.7 76.1 6.8

1 elbow 73.2 17.2 68.2 7.0 68.0 13.7 68.3 7.1 71.6 17.3 68.3 7.1

shoulder 68.7 34.1 52.5 7.6 64.9 28.1 52.5 8.0 68.6 27.2 52.5 8.4

wrist 71.6 1.3 76.1 6.8 77.0 1.8 76.1 6.8 68.4 2.0 75.5 6.8

2 elbow 55.0 6.4 68.4 7.0 50.5 6.9 68.4 7.1 45.7 8.2 63.4 7.1

shoulder 58.5 24.9 52.5 7.5 52.9 21.6 52.5 7.9 61.0 16.7 51.6 10.3

wrist 76.0 3.7 76.1 6.8 76.1 4.1 76.2 6.8 75.4 4.3 76.0 6.8

3 elbow 69.4 12.3 68.4 7.0 71.7 18.2 68.5 7.1 71.0 14.9 68.2 7.1

shoulder 47.8 19.8 52.5 7.5 46.5 20.4 52.5 7.9 54.1 21.7 52.5 8.3

wrist 75.2 1.9 76.1 6.8 70.5 2.2 76.2 6.9 67.6 1.9 76.2 6.8

4 elbow 74.7 16.6 68.4 7.0 69.5 12.7 68.6 7.2 68.9 18.4 68.5 7.1

shoulder 67.8 34.7 52.5 7.5 61.9 30.6 52.5 8.2 65.8 28.9 52.5 8.3

wrist 73.0 1.1 76.1 6.8 72.2 1.3 75.8 6.8 69.0 2.1 76.2 6.8

5 elbow 65.3 4.4 68.4 7.0 54.7 2.2 66.2 7.1 46.0 8.3 68.5 7.1

shoulder 63.2 21.1 52.5 7.5 55.8 20.6 46.7 7.9 60.3 16.1 52.5 8.3

wrist 76.0 3.7 76.1 6.8 75.9 3.9 75.9 6.8 75.5 4.3 75.5 6.8

6 elbow 69.3 12.3 68.3 7.1 72.7 15.5 66.2 7.0 71.2 14.9 63.4 7.1

shoulder 47.9 19.9 52.5 7.9 47.1 18.1 46.6 7.8 54.0 21.5 51.6 10.3
Table 5 –Percentage of joint moment and force reduction using the electronic control and resultant of the mechanical 

control comparing right knee work posture and operator percentile versus joystick direction and body joint 

Operator percentile for right knee 

05 50 95

Joint

moment Joint force Joint

 moment Joint force Joint

 moment Joystick Joint force 

direction

Body 

joint % Nm % N % Nm % N % Nm % N

wrist 78.3 2.5 75.7 6.8 73.6 2.3 75.9 6.8 74.4 2.6 75.9 6.8

1 elbow 74.5 16.6 66.0 7.0 63.1 11.5 66.2 7.0 61.2 12.0 66.2 7.1

shoulder 77.8 20.3 46.8 7.5 69.3 24.0 46.7 7.9 84.9 19.1 46.7 8.3

wrist 82.9 1.5 75.8 6.8 67.8 1.3 75.9 6.8 91.2 3.9 75.9 6.8

2 elbow 49.3 3.1 66.2 7.0 63.5 11.6 66.3 7.0 79.2 11.0 66.3 7.1

shoulder 74.2 15.8 46.8 7.5 58.4 21.2 46.7 7.8 78.2 20.8 46.7 8.3

wrist 76.9 3.5 75.8 6.8 75.7 3.8 75.9 6.8 75.2 3.9 75.9 6.8

3 elbow 73.8 12.0 66.0 6.9 73.1 21.0 66.2 7.0 71.3 10.6 66.3 7.1

shoulder 71.9 17.0 46.8 7.5 57.2 24.4 46.7 7.9 69.1 23.3 46.7 8.4

wrist 73.6 2.6 75.4 6.8 74.7 2.2 75.8 6.8 71.9 2.6 75.9 6.8

4 elbow 73.2 16.7 65.5 6.9 72.0 8.4 66.1 7.1 62.5 10.2 66.3 7.2

shoulder 74.4 21.3 47.1 7.4 63.7 27.7 46.8 7.9 66.1 24.9 46.7 8.4

wrist 69.7 1.7 75.5 6.8 69.9 1.2 75.7 6.8 58.1 1.2 75.9 6.8

5 elbow 46.7 4.9 65.6 6.9 71.4 8.9 66.0 7.1 52.0 7.3 66.3 7.2

shoulder 76.8 15.5 47.0 7.4 60.1 16.9 46.8 8.0 66.9 21.0 46.7 8.4

wrist 75.5 3.6 75.8 6.8 75.9 3.8 75.8 6.8 74.9 3.9 75.8 6.8

6 elbow 70.1 12.7 66.2 7.0 74.5 20.4 66.2 7.0 72.3 11.0 66.2 7.1

shoulder 80.7 15.3 46.7 7.5 70.3 13.6 46.7 7.9 74.7 20.8 46.7 8.3

Table 6 –Percentage of joint moment and force reduction using the electronic control and resultant of the mechanical 

control comparing standing work posture and operator percentile versus joystick direction and body joint 

Operator percentile for standing 

05 50 95

Joint

moment Joint force Joint

moment Joint force Joint

 moment Joystick Joint force 

direction

Body 

joint % Nm % N % Nm % N % Nm % N

wrist 75.6 2.4 75.4 6.8 75.0 3.5 75.5 6.8 72.1 3.4 75.4 6.8

1 elbow 74.1 8.1 63.2 7.0 73.1 12.8 63.4 7.0 70.1 15.1 63.3 7.1

shoulder 75.9 16.5 40.1 7.5 79.2 23.2 39.8 7.9 74.6 27.9 39.9 8.3

wrist 52.8 0.3 75.5 6.8 58.2 0.6 0.9 6.8 61.5 1.8 75.5 6.8

2 elbow 64.6 5.6 63.4 7.0 63.7 8.7 11.6 7.0 62.7 15.5 63.4 7.1

shoulder 70.5 15.6 39.9 7.6 72.3 22.2 24.6 7.8 65.7 30.5 39.9 8.3

wrist 75.2 2.0 75.4 6.8 74.0 2.7 75.4 6.8 73.3 3.7 75.5 6.8

3 elbow 64.9 5.8 63.3 7.0 62.3 11.0 63.2 7.0 67.9 12.2 63.4 7.1

shoulder 59.5 10.6 40.0 7.6 62.6 22.6 40.1 7.8 58.4 31.4 39.9 8.3

wrist 76.1 2.4 75.4 6.8 76.0 3.5 75.3 6.8 77.3 3.1 75.5 6.8

4 elbow 74.7 8.0 63.3 7.0 74.4 12.3 63.2 7.0 76.7 12.8 63.5 7.1

shoulder 65.8 17.6 40.0 7.5 64.5 31.0 40.2 7.8 61.9 38.5 39.8 8.3

wrist 54.9 0.2 75.4 6.8 66.2 0.6 75.3 6.8 92.7 1.1 75.5 6.8

5 elbow 68.8 5.4 63.3 6.9 72.6 9.1 63.1 7.0 93.3 10.1 63.5 7.1

shoulder 67.1 16.1 40.0 7.5 66.0 28.2 40.3 7.8 63.7 32.7 39.8 8.2

wrist 75.8 2.0 75.5 6.8 75.6 2.7 75.5 6.8 76.5 3.5 75.6 6.8

6 elbow 68.4 5.3 63.6 7.0 72.1 8.2 63.4 7.0 79.1 14.1 63.5 7.1

shoulder 65.1 10.9 40.3 7.5 67.4 18.9 39.9 7.9 67.6 21.9 39.7 8.2