ARTICLE IN PRESS

Journal of Biomechanics 42 (2009) 1570–1573

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Journal of Biomechanics

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Short communication

Robot-based methodology for a kinematic and kinetic analysis of

unconstrained, but reproducible upper extremity movement

Nikica Popovic a,, Sybele Williams b, Thomas Schmitz-Rode a, Gu¨nter Rau a,

Cantherine Disselhorst-Klug a

a Department of Applied Medical Engineering, Helmholtz Institute, RWTH Aachen University, Germany b Department of Physics, The University of the West Indies, St. Augustine, Trinidad and Tobago a r t i c l e

i n f o

a b s t r a c t

Article history:

Although arm movements play an important role in everyday life, there is still a lack of procedures for Accepted 27 March 2009

the analysis of upper extremity movement. The main problems for standardizing the procedure are the variety of arm movements and the difficult assessment of external hand forces. The first problem Keywords:

requires the predefinition of motions, and the second one is the prerequisite for calculation of net joint Upper extremity

forces and torques arising during motion. A new methodology for measuring external forces during Movement analysis

prespecified, reproducible upper extremity movement has been introduced and validated. A robot-arm Reproducibility

has been used to define the motion and 6 degrees of freedom (DoF) force sensor has been attached to it Kinematics

for acquiring the external loads acting on the arm. Additionally, force feedback has been used to help Kinetics

keeping external loads constant. Intra-individual reproducibility of joint angles was estimated by using correlation coefficients to compare a goal-directed movement with robot-guided task. Inter-individual reproducibility has been evaluated by using the mean standard deviation of joint angles for both types of movement. The results showed that both inter- and intra-individual reproducibility have significantly improved by using the robot. Also, the effectiveness of using force feedback for keeping a constant external load has been shown. This makes it possible to estimate net joint forces and torques which are important biomechanical information in motion analysis.

& 2009 Elsevier Ltd. All rights reserved.

1. Introduction

However, there is a lack of methods for the assessment of

arbitrary upper extremity movements, which are not restricted or

Today, the standardised measurement of both three-dimen-

repeatable, as compared to the movement’s characteristic of gait

sional kinematics and kinetics together with muscle activity using

(Rau et al., 2000). Many robot-assisted methods which can be

surface EMG (SEMG) is the usual procedure in clinical gait

end-effector-based (Hogan et al., 1995; Krebs et al., 1998; Burgar

analysis (Chambers and Sutherland, 2002). Motion analysis

et al., 2000) or in form of an exoskeleton (Sanchez et al., 2006; Nef

systems in combination with underlying biomechanical rigid

et al., 2007) have been used in rehabilitation for arm therapy.

segment models (Kadaba et al., 1990; Davis et al., 1991) have been However, there are no reports on using robots in the motion

used to calculate joint angles. From these, other kinematic data

analysis of upper extremities. The reason that disqualifies them

such as joint velocity and acceleration of lower extremity

from being used as a standard procedure in movement analysis is

movements can be determined. For the kinetic description of

at least one of the following limitations: the investigated move-

motion it is necessary to measure the forces acting on the body

ment cannot be arbitrary, the movement is 2D, range of motion is

during movement. In gait analysis, those external forces are

limited, the method cannot be applied for activities of daily living,

commonly acquired using force plates which detect the ground-

movement in one joint is disabled or the arm joint chain is not

reaction forces. The kinematic and kinetic data can then be used

free.

as inputs for a kinetic model (Bresler and Franke, 1950; Cavagna

Additionally, in contrast to gait, the external forces that are

and Magaria, 1966), which calculates net joint moments and net

compensated by the neuromuscular system are less defined and

joint forces.

have lower magnitudes. As a consequence, information about the

forces and torques acting on the joints during upper extremity

movements is often unavailable. Furthermore, the interpretation

of the muscular-coordination pattern depicted by SEMG becomes



complex and sometimes impossible. Human arm dynamics have

Corresponding author. Tel.: [phone]; fax: [phone].

E-mail address: [e-mail] (N. Popovic).

been less investigated than the kinematics and the procedures

0021-9290/$ - see front matter & 2009 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jbiomech.2009.03.042





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were either task specific where the upper extremity kinetics has

the target force vector has been attained. As such the star has become green and is

been analysed during crutch-assisted gait (Requejo et al., 2005) or positioned in the central circle.

The experiments were performed on the dominant arm of eight subjects

during wheelchair propulsion (Ensminger et al., 1995); or without

(5 male and 2 female) who participated in the study. They were all healthy, ages

measured external loads (Riener and Straube, 1997).

22–32, and gave informed consent prior to the experiments.

For these reasons, there is a need for a methodology that not

only improves the reproducibility of upper extremity movements

but also defines and measures the external forces during any

3. Validation

freely definable upper extremity movements.

3.1. Reproducibility of joint angles

2. Method

For validation of the reproducibility of joint angles, a goal-

directed movement was compared with the same motion guided

To enhance the reproducibility of upper extremity movement, 6 degrees of

freedom (DoF) KUKA robot-arm (Fig. 1) was used to predefine the motion. For the by the robot. For this purpose, a relatively complex, three-measurement of the external forces on the robot’s end-effector a 6 DoF force

dimensional daily activity referred to as â€ÅšRemoving a parking

sensor with a ball-shaped handle had been attached. The subject held this handle

token’ (Williams et al., 2006) has been chosen. The subject was

during the movement test. Additionally, a force feedback about the current

asked to perform three times the sequence of movements

external load provided by a display connected to the sensor has been used to

required to remove a parking token from a dispenser at the car-

maintain a predefined force vector.

The display acts as a tool, which allows the definition of a target force in all

park, from a seated position in a car. The robot-guided movement

degrees of freedom as well as a visualisation of the difference between the target

was performed using the preprogrammed 3D motion path, also

and applied force vector. This target vector can be either constant or variable

with three motion cycles. Both trials were repeated at least a day

during a movement test. The insert in Fig. 1. shows two cases which may be after the first measurement. For this movement, all three shoulder

displayed.

axes and flexion/extension axis in elbow joint are well defined,

On the left-side, the applied force should be corrected since the target force

vector is not achieved. The vector resulting from the difference between the two

while the two hand axes and elbow pronation/supination axis are

force vectors is presented on the screen as a black star. The position of the star on

left free for subject to choose whether to use them or not. The

the screen depends on the manipulation of the handle by the subject and

joint angles were calculated (Schmidt et al., 1999; Williams et al.,

simultaneously indicates the direction in which the applied force vector should be

2006) for the shoulder joint and flexion/extension axis in elbow

corrected in order to move it into the target circle. On the right-side of the insert,

joint for each trial.

The intra-individual reproducibility of the movement was

evaluated using the Pearson product–moment correlation coeffi-

Force sensor

cients between the two independent trials for each rotational axis

of shoulder and flexion/extension of elbow joint. Table 1 shows

the mean values and standard deviations of the correlation

coefficients obtained from the trials performed by 7 subjects.

The mean values of the correlation coefficients (Table 1)

obtained for the robot-guided movement (0.66–0.87) were

Handle

significantly higher (po0.001) than those for the goal-directed

movement (0.42–0.56). The ranges of the standard deviations of

the mean correlation coefficient were 0.11–0.27 and 0.37–0.45,

respectively.

In order to test the inter-individual variations in joint angles,

the mean values and standard deviation of the second repetition

in both trials have been calculated. The mean values of the

standard deviations from 8 subjects for each measured joint axis

were determined. Table 2 shows that they were significantly

smaller (po 0.036) for the guided movement (7.28–21.781) than

for the goal-directed movement (9.59–27.51).

Robot arm

3.2. Validation of the force feedback

Black star

Visual Feedback

Green star

For validation of the force feedback, 8 subjects performed three

repetitions, with and without force feedback, of a shoulder flexion

Table 1

Mean values and standard deviations of the correlation coefficients of joint angles

between two trials for the goal-directed and robot-guided task.

Target circle

Movement

Goal directed

Robot guided

Wrong

Right

Correlation coefficients (mean value with standard deviation)

Shoulder

Flex/ext

0.5670.39

0.8170.22

Abd/add

0.5570.37

0.8770.11

Fig. 1. Measurement system: a robot-arm presents a 3D path, 6 DoF force/torque

Inn/out

0.4270.45

0.6670.27

sensor attached at the end effector and a handle used as a user interface between a

subject and the force/torque sensor. Force feedback helps in maintaining a

Elbow

Flex/ext

0.5270.39

0.7970.24

predefined force vector. Insert: on the left-side, the applied force should be

corrected (target force vector is not achieved, the star is outside the target circle

The flexion/extension (flex/ext), abduction/adduction (abd/add) and inner/outer

and black); on the right-side, target force vector is achieved (the star is in the

rotation (inn/out) axes of the shoulder joint and the flexion/extension (flex/ext)

target circle and green).

axis of the elbow joint were considered.





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N. Popovic et al. / Journal of Biomechanics 42 (2009) 1570–1573

Table 2

4. Discussion

The mean values of the standard deviations of the second repetition for both tasks

from 8 subjects for each measured joint axis.

The results show that both intra- and inter-individual differ-

ences in joint angles decreased using predefined robot paths. In

Movement

Goal directed

Robot guided

contrast to goal-directed tasks, the procedure developed allows,

Standard deviation (mean value)

both the preprogramming of the desired test path, which allows

Shoulder

Flex/ext

717.21

714.691

guidance during the complete movement, and also the control of

Abd/add

79.591

77.281

velocity in each part of the movement. The ability to calculate the

Inn/out

721.451

714.161

joint angles for the complete joint chain of the arm facilitates not

Elbow

Flex/ext

727.51

721.781

only monitoring of the functionality of the joint investigated, but

The flexion/extension (flex/ext), abduction/adduction (abd/add) and inner/ outer

also the analysis of individual movement strategies. It can thus

rotation (inn/out) axes of the shoulder joint and the flexion/ extension (flex/ext)

provide an answer to a number of questions e.g. â€ÅšHow is an injury

axis of the elbow joint were considered.

of one joint compensated for in other joints?’.

Introducing the force sensor with force feedback, a measure-

ment system has been created, which allows the functional

testing of upper extremity movement performance. This includes

the assessment of movement kinematics as well as the measure-

ment of external loads. These data can be further used in

biomechanical models to calculate kinetic data such as net joint

forces and net joint moments.

By utilizing this procedure, it will be possible to more fully

compare reproducible, unconstrained movements of upper ex-

tremities. Therefore, normal and/or patient collectives can be

formed and compared. Additionally, comparisons can be made

between a patient and a normal collective. This information can

be used to establish the movement patterns and compare the

ranges of motion characteristic of different patient groups. In

combination with SEMG, this procedure can be used to illustrate

the muscular-coordination patterns at different contraction levels.

It could be used for stroke patients, patients with plexus lesion

or patients with other upper extremity disorders and injuries. The

main principles would remain the same, but some changes in the

procedure such as tracking task or holding the robot’s handle,

choosing the appropriate robot path and velocity or the appropriate

force level, have to be made to adapt to the patient’s group or age.

Through movement standardization, the ability to compare

data that will be used for evidence-based decision-making or the

evaluation of rehabilitation programs is greatly improved. As such,

this methodology has a direct impact on clinical applications for

patients suffering from upper extremity disorders.

Conflict of interest statement

The authors would like to disclose any financial and personal

relationships with other people or organizations that could

inappropriately influence their work.

Acknowledgment

The authors gratefully acknowledge the financial support

Fig. 2. Forces in the z-axis for 8 subjects, in addition to the mean values and

provided by the German Research Council (Deutsche Forschungs-

standard deviations (a) without and (b) with force feedback.

gemeinschaft DFG) (DI 596/4-1; DI 596/4-2).

References

movement with a target force of 15 N upwards. For the measure-

ment without feedback, the subjects were instructed to try to

Bresler, B., Franke, J.P., 1950. The forces and moments of the leg during level

generate the same force as in previous measurement with

walking. American Society of Mechanical Engineers Transactions 72, 27–36.

feedback without information about the currently generated

Burgar, C.G., Lum, P.S., Shor, P.C., Van der Loos, H.F.M., 2000. Development of robots

loads. Fig. 2 shows the external force measured in the z-axis for

for rehabilitation therapy: the Palo Alto VA/Stanford experience. Journal of

Rehabilitation Research and Development 37 (6), 663–674.

the 8 subjects, the mean values and standard deviations obtained

Cavagna, G.A., Magaria, R., 1966. Mechanics of walking. Journal of Applied

with (a) and without force feedback (b).

Physiology 21, 271–278.

Fig. 2 shows that when using force feedback, the mean values

Chambers, H.G., Sutherland, D.H., 2002. A practical guide to gait analysis. Journal of

the American Academy of Orthopaedic Surgeons 10 (3), 222–231.

of the external force fluctuate less and the standard deviation

Davis, R.B., O

ËÅ› unpuu, S., Tyburski, D., Gage, J.R., 1991. A gait analysis data collection

decreases.

and reduction technique. Human Movement Science 10 (5), 575–587.

ARTICLE IN PRESS

N. Popovic et al. / Journal of Biomechanics 42 (2009) 1570–1573

1573

Ensminger, G.J., Robertson, R.N., Cooper, R.A., 1995. A model for determining 3-D

Requejo, P.S., Wahl, D.P., Bontrager, E.L., Newsam, C.J., Gronley, J.K., Mulroy, S.J.,

upper extremity net joint forces and moments during wheelchair propulsion.

Perry, J., 2005. Upper extremity kinetics during Lofstrand crutch-assisted gait.

Engineering in Medicine and Biology Society, IEEE 17th Annual Conference,

Medical Engineering & Physics 27, 19–29.

September 20–23, Montreal, Canada, vol. 2, pp. 1179–1180.

Riener, R., Straube, A., 1997. Inverse dynamics as a tool for motion analysis: arm

Hogan, N., Krebs, H.I., Sharon, A., Charnnarong, J., 1995. Interactive robotic

tracking movements in cerebellar patients. Journal of Neuroscience Methods

therapist. US patent, 5466213.

72, 87–96.

Kadaba, M.P., Ramakrishna, H.K., Wotten, M.E., 1990. Measurement of lower

Sanchez, R.J., Liu, J., Rao, S., Shah, P., Smith, R., Rahman, T., Cramer, S.C., Bobrow, J.E.,

extremity kinematics during level walking. Journal of Orthopaedic Research 8

Reinkensmeyer, D.J., 2006. Automating arm movement training following

(3), 383–392.

severe stroke: Functional exercises with quantitative feedback in a gravity-

Krebs, H.I., Hogan, N., Aisen, M.L., Volpe, B.T., 1998. Robot-aided neurorehabilita-

reduced environment. IEEE Transactions on Neural Systems and Rehabilitation

tion. IEEE Transactions on Rehabilitation Engineering 6, 75–87.

Engineering 14 (3).

Nef, T., Mihelj, M., Colombo, G., Kiefer, G., Perndl, C., Muller, R., Riener, R., 2007.

Schmidt, R., Disselhorst-Klug, C., Silny, J., Rau, G., 1999. A marker-based

ARMinâ€"Exoskeleton for Arm Therapy in Stroke Patients. In: Proceedings of the

measurement procedure for unconstrained wrist and elbow motions. Journal

2007 IEEE 10th International Conference on Rehabilitation Robotics, June

of Biomechanics 32 (6), 615–621.

12–15, Noordwijk, The Netherlands.

Williams, S., Schmidt, R., Disselhorst-Klug, C., Rau, G., 2006. An upper body model

Rau, G., Disselhorst-Klug, C., Schmidt, R., 2000. Movement biomechanics goes

for the kinematical analysis of the joint chain of the human arm. Journal of

upwards: from the leg to the arm. Journal of Biomechanics 33 (10), 1207–1216.

Biomechanics 39 (13), 2419–2429.





Document Outline


Robot-based methodology for a kinematic and kinetic analysis of unconstrained, but reproducible upper extremity movement Introduction

Method

Validation Reproducibility of joint angles

Validation of the force feedback





Discussion

Conflict of interest statement

Acknowledgment

References