Evaluation of Motion Controlled Arm Support

A. Bergsma

1,2

, L. A. van der Heide

3,4

, J. M. N. Essers

, A. Murgia

, D. van der Pijl

, E. C. Cup

G. J. Gelderblom

, P. H. Verstegen

, I. J. M. de Groot

and K. Meijer

Maastricht University Medical Centre+, Department of Human Movement Sciences, Maastricht, The Netherlands

Radboud University Nijmegen, Medical Centre, Nijmegen Centre for Evidence based Practice,

Department of Rehabilitation, Nijmegen, The Netherlands

Zuyd University of Applied Sciences Heerlen, Research Center for Technology in Care, Heerlen, The Netherlands

Maastricht University, School for Public Health and Primary Care (CAPHRI), Maastricht, The Netherlands

University Medical Center Groningen, Center for Human Movement Sciences, Groningen, The Netherlands

FOCAL Meditech Bv, Tilburg, The Netherlands

1 OBJECTIVES

Assistive devices augmenting arm functionality in

the weak upper extremity were introduced

extensively after the polio epidemic in the 1940s.

Many attempts have been made to help people with

upper extremity limitations in daily life by means of

dynamic arm supports. Current devices vary from

passive supports, with low level of complexity and

easy to control, providing limited support in the

performance of ADL tasks to active arm supports

with many functionalities, large dimensions and

complex control (van der Heide, 2013). An example

of a new development is the motion-controlled arm

support (McArm) that aims to enhance the

functional benefit of support while maintaining the

user friendliness of the simple passive support

systems. In addition, it aims to stimulate the use of

residual muscle strength in the user (Focal Meditech

BV, 2013).

The effect of support on human arm movements

needs to be investigated to understand how support

can be optimized. The influence of assistive devices

that compensate weakened muscles on the

restoration of arm functions after stroke, has been

investigated by Prange et al. (Prange, 2009). They

have studied the effect of gravitational pull of arm

support systems with braces. They found that the

activity level of shoulder and upper arm muscles

during reaching movements using a forearm support

system are significantly decreased. However there is

still little evidence on how joint moments are

changed by a support system and whether zero

gravity support is the best biomechanical solution

that designers should aim for. Moreover, it is still

unclear how joint moments are affected in people

suffering from for example neuromuscular diseases

and how people perform with an arm support in a

daily life situation. Information on the effects of

dynamic arm supports on arm movements and use of

arm supports in daily life is needed to provide

insight in the limitations of current designs. That

knowledge can be used in the development of new

motion controlled arm support.

The aim of this study was to investigate the

impact of arm support systems on the arm function

and use in daily life. This was realized by studying

the effect of an available support system on the arm

function at various levels of the International

Classification of Functioning, Disability and Health

(ICF). This knowledge is integrated in the design of

the new McArm.

2 METHODS

A test battery has been developed that combines

questionnaires and ordinal clinical scales, with

quantitative measures such as 3D motion analysis

and EMG to provide a more complete picture of the

compensatory movement patterns used by patients

with proximal muscle weakness of the upper

extremities in patients with neuromuscular disorders.

Moreover, biomechanical models and inverse

dynamic software were used to calculate the

shoulder and elbow joint moments in three different

conditions (a control set-up, a gravity compensation

set-up and a simulated zero gravity environment).

These measures were used to investigate movement

capacities of people with various neuromuscular

disorders. To evaluate the performance in daily life,

a measurement protocol has been developed to

measure how people use the arms and the arm

support during daily activities.

The following paragraphs describe the structure

of the various measures and the first results.

Bergsma A., van der Heide L., M. N. Essers J., Murgia A., van der Pijl D., C. Cup E., J. Gelderblom G., H. Verstegen P., J. M. de Groot I. and Meijer K..

Evaluation of Motion Controlled Arm Support.

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

2.1 Questionnaires

A web-based questionnaire containing questions on

all ICF domains, was composed to evaluate overall

arm function and problems people encounter in daily

life. This questionnaire was distributed among

various groups of people with neuromuscular

diseases, namely Duchenne muscular dystrophy

(DMD), fascioscapulohumeral dystrophy (FSHD),

limb-girdle muscular dystrophy (LGMD) and spinal

muscular atrophy (SMA).

2.2 3D Kinematics and EMG

Motion analysis and electromyography (EMG) data

from various tasks (e.g. shoulder abduction/flexion,

reaching and hand to mouth movement) were

recorded during unsupported movement and during

supported movement with a passive Sling arm

support (Focal Meditech). In both cases the subject

was asked to move the dominant hand from an initial

position resting on a table in the sagittal plane to a

target placed at a distance of a stretched arm, at

shoulder height and one shoulder width on the

ipsilateral side. The movements were recorded with

a 3D camera Motion Capture system (Vicon).

Reflective markers were attached on the subject’s

body following the guidelines of the Vicon Upper

Limb model. These data were subsequently used in

simulations with a multi-body model of the arm to

calculates joint moments. EMG data were obtained

from biceps brachii, deltoid, triceps brachii,

trapezius, pectoralis and latissimus dorsi muscles

and were normalized as percentage of the EMG

during maximum voluntary contraction.

2.3 Muscle-skeletal Simulations

The coordinates of the reflective markers during the

unsupported and supported ipsilateral reaching

movements were used to drive the simulation model

in the AnyBody Modeling System (AnyBody

Technology). With the subject’s anthropometric

information derived from marker coordinates, the

software’s GaitFullBody model was scaled

according to body length and mass among others.

An inverse dynamic analysis was then carried out to

calculate the net joint moments at the shoulder and

elbow. The analysis on the unsupported movement

consisted of two parts: a normal gravity situation

and a simulated zero gravity situation, in which the

same motion data for the unsupported movement

were used but gravity was set to zero in AnyBody’s

model parameters. As a result the outputs of the

calculation were the net joint moments in three

conditions: I control, II gravity compensation with

Sling and III zero gravity environment. These

conditions were chosen to assess the influence of

gravity compensation (I vs. II), the influence of a

zero gravity environment (I vs. III) and the

difference between gravity compensation induced

either by a mechanism or resulting from a zero

gravity environment (II vs. III)(Essers, 2013).

2.4 Ambulatory Performance

To evaluate the effect of arm supporting devices in a

daily life setting, a protocol for monitoring the arm

activity outside a laboratory setting was developed.

A tri-axial accelerometer (MOX, Maastricht

Instruments) was placed on the upper arm just above

the elbow. The acceleration signals were post-

processed to obtain elevation and intensity of upper

arm movements. These data give an indication on

how and how often the arm support is used in daily

life (Annegarn, 2012).

3 RESULTS

Preliminary results for the various studies are

shown.

3.1 Questionnaires

In total of 315 boys/men with DMD, 88 with FSHD,

61 with LGMD and 73 with SMA participated.

Preliminary data show that pain, stiffness and

functional limitations increased with age in DMD.

Data of FSHD, LGMD and SMA are being

analysed.

3.2 3D Kinematics and EMG

The maximum shoulder elevation angles and the

minimal and maximal elbow flexion angles were

analysed in a group of 11 people with FSHD and in

a group of 8 healthy controls. The data depicted in

figure 1 represent the shoulder elevation angles of

the healthy control group and the data of the FSHD

subjects. Significant differences between the

shoulder angles of the FSHD and the healthy control

group were found for the shoulder elevation angle

during the abduction and flexion tasks and during

the two reaching tasks. Of the 11 subjects, only two

were able to elevate the arm above 90 degrees. The

EMG data showed higher percentages in the FSHD

FSHD group compared to the control group.

3.3 Muscle-skeletal Simulations

The ipsilateral reaching task was completed by all

subjects in all conditions. The FSHD subjects

required more time to complete the task in the

Control and the Sling condition than the healthy

group (respectively 2.6 vs. 3.7s and 2.8 vs. 4.7s).

Both groups required more time to complete the task

in the Sling condition than in the control situation. In

the control situation, the maximum value of the

moment was greater by more than one order of

magnitude than the moment in the Sling and the

Zero gravity conditions in both groups (Figure 2).

Between the two groups the signs of the average

moments in the Sling condition were different,

showing for the FSHD group a trend to maintain the

arm more elevated and the elbow more flexed. The

healthy group presented a lower mean moment in

the Sling condition than the FSHD group, showing a

trend to maintain the arm less elevated and the

elbow more extended when using the Sling (Essers,

2013).

Figure 1: maximum shoulder elevation angle of shoulder

abduction. The grey band represents the 95% confidence

interval of the control group, the dashed lines represent the

average of the control group and the continuous lines

represent a group of 11 FSHD subjects.

Figure 2: Joint Moments of glenohumeral abduction-

adduction.

3.4 Ambulatory Performance

In a group of 12 healthy men, the activity of the arm

that was performed in one day was measured. 40%

of the total upper arm activity was categorized as

low intensity and low elevation. Less than 2% was

classified as high elevation. The average number of

elevations above 90 degrees was for most subjects

less than 10 times per hour.

4 DISCUSSION

The current study presents some preliminary results

of initial evaluation measures. Application of these

evaluation measures for the next McArm prototype

is foreseen for the last stage of the project. The goals

of these evaluations are multiple: to see if design

goals and specifications are met, to gain first

outcomes on usage and usability of the new device,

and compare functionality with high- end existing

devices of this class. For this purpose several

existing measurement scales and instruments were

combined into a specific set. The availability of such

a set will be applicable for evaluation of support

systems.

ACKNOWLEDGEMENTS

The project McArm is a national Dutch ‘Pieken in

de Delta’ project, partially funded by Agentschap

NL.

REFERENCES

Annegarn, J. 2012. Assessment Of Intensity And

Elevation of Arm Movements during Activities of

Daily Life. Daily Activity Monitoring In Patients With

Copd.

Essers, J. M. N., Murgia, A., Bergsma, A., Verstegen, P.

H., Meijer, K. An Inverse Dynamic Analysis on the

Influence of Upper Limb Gravity Compensation

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