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Ergonomics In Motion
The science of ergonomics has been
utilized in the past as a predominately
reactive process to evaluate the risk of
injury on existing jobs. Jobs were
designed, injuries occurred due to some
of the characteristics of the jobs
resulting in a redesign of the tasks and
their associated risk (Potvin, Chiang,
Jones, McInnes & Stephens, 2008).
More recently, digital human modeling
technology has been used to analyze and
improve the physical ergonomics of a
variety of tasks with the idea of
reducing total design time and
engineering costs (Chaffin, 2001).
Ergonomic engineers create avatars, or
virtual humans, with specific
anthropometrics to be inserted into
three dimensional virtual environments
to complete their ergonomic evaluations
(Du & Duffy, 2007).Using this
methodology, risk factors for injury can
be addressed early in the design stage
before task parameters are finalized.
One of the limitations with using
digital human models is that engineers
have to make assumptions as to how a
worker would interact with the virtual
workplace (Potvin et al, 2008). If
workers are readily available to provide
insight and input, or if the engineer
has detailed knowledge and understanding
of the process being analyzed, than this
would not be a significant factor.
However, if the process/task being
analyzed is new or not well understood,
this could affect the results when
completing an ergonomic evaluation as to
the acceptability of a task or creating
force targets.
Research has shown that a deviation of
20° in a joint angle could alter the
prediction of the population capability
by as much as 50% (Chaffin & Erig,
1991). Motion capture technology can be
a valuable resource when attempting to
obtain realistic postures and motions of
humans optimizing their bodies and
environments. The reliability of using
digital human models for quantifying
ergonomic risk and strength limits is
enhanced when using this type of
technology (Du et al, 2007).
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There are certain components that
need to be acquired in order to
perform optical motion capture
studies. Firstly, an
appropriate number of digital
cameras equipped with infrared (IR)
filters and near IR light rings
(Figure 1) must be obtained. The
number of cameras needed to
effectively track motion depends on
the capture volume (i.e. size of the
room) and what type of motion is
being tracked (i.e. golf swing vs.
installing a center console into a
vehicle). The resolution of the
digital cameras (i.e. 640 x 480
pixels, 2352 x 1728 pixels) and the
operating rate (i.e. frames per
second, [fps]), should also be
considered.
In general, higher resolution
cameras will be more accurate in
tracking the reflective markers in
space. The faster the motion to be
tracked the higher the operating
rate required to account for the
speed of movement and to acquire
data. An example that addresses
both issues of the number of cameras
and the operation rate required
would be comparing a golf swing to
installing a vehicle center
console. A golf swing is a fast
motion and would most likely be done
in a wide open area with no
obstructions, resulting in the need
for fewer cameras.
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Figure 1: IR Camera |
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In the case of installing a center
console, more cameras would be
required because of the many
obstructions involved (i.e. vehicle
side panels, instrument panel, car
seats, etc.). In terms of operating
rate (fps), the golf swing would
need a higher rate than the center
console install as the golf swing
would be completed much faster. |
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Figure
2: Human subject with full marker
set on, preparing for a motion
capture study |
Secondly, reflective markers are
placed on moving objects so the
cameras can track their motion.
Ultimately, the near IR light coming
off the ring reflects off the
markers and back into the camera via
the IR filter lens. The more
degrees of freedom an object has,
the more reflective markers should
be used. For example, a human may
have approximately 50 markers on
them (Figure 2) where a center
console may have approximately 5
markers. |
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In order to make the
cameras and markers all relevant,
the third essential component is to
use software created to work with
the applicable camera system.
The software plays a key role
because that is where the 3-D marker
clouds are represented; markers can
be identified and segments linked to
create a skeleton template. (Figure
3) |
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Subject tracked in real time |
Cloud
of markers |
Markers identified, segments created |
Skeleton template created |
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Figure
3 |
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Once
the skeleton template has been created a
digital human model (DHM), the fourth
major component to utilizing motion
capture technology, is needed to perform
ergonomic evaluations; including
positioning and analyzing humans
performing different tasks in different
environments.
SiemensTM JACK digital
human modeling and ergonomic analysis
software provides a motion capture
toolkit which drives the digital human
manikin (Figure 4) with the collected
motion data in its three-dimensional
interactive environment. Several
traditional ergonomic analysis tools are
embedded in JACK and are used to
quantify risk of injury, evaluate design
solutions and proposals, model
workstations, interfaces, etc. (Du et
al, 2007).
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Motion capture technology also
allows for real-time and offline
streaming of motion along with
capturing head movements that can be
used to generate eye views within
the JACK software. To get a true
immersive feeling, a head mounted
display (HMD) can be used to allow
subjects to look through the eyes of
a DHM (Figure 5). Other benefits of
this technology include human
scaling of manikins, quick realistic
movements for visualization,
capturing part/tool paths and true
representations of human postures
and movements. |
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Figure
5: Head Mounted Display |
Whether motion capture technology is
being applied in the movie making
business, video game industry, or the
manufacturing and product design arena,
its advantages are immense. The use of
this technology can elevate your
analysis or imaging to the next level of
reality. With a society looking to
reduce waste and work smarter, consider
the numerous applications motion capture
and DHMs could have to improve safety,
quality and productivity in the
workplace.
References
Chaffin, D.B. (2001) Digital Human
Modeling for Vehicle and Workplace
Design. Society of Automotive
Engineers, Warrendale, PA.
Chaffin, D.B., Erig, M. (1991).
Three-dimensional biomechanical static
strength prediction model sensitivity to
postural and anthropometric
inaccuracies. IIE Transactions,
23 (3), 215-217.
Du, J., Duffy, V. (2007). A methodology
for assessing industrial workstations
using optical motion capture integrated
with digital human models.
Occupational Ergonomics, 7, 11-25.
Potvin, J.R., Chiang, J., Jones, M.,
McInnes, B., Houston, A., Stephens, A.
(2008) Proactive Ergonomic Analyses
with Digital Human Modeling: A
Validation Study. Proceedings of the
2008 North American Congress on
Biomechanics Conference, Ann Arbor, USA,
August 2008.
EVaRT is a trademark of Motion Analysis
Corporation. Additional information can
be found at:
http://www.motionanalysis.com/
Jack software is copyright of
Siemens Product Lifecycle Management
Software Inc.
Contact
Enrico Fiacco BHK, MHK
rfiacco@sandalwood.com
Sandalwood
is an engineering and ergonomics
consulting firm. Sandalwood designs and
executes strategic programs for
manufacturers which reduce their
work-related risks to quality,
productivity and employee health. By
providing knowledge, research,
technology and resources, Sandalwood
supports its clients from the executive
level to the factory floor.
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our customers and friends for the
benefit of the Ergonomics community and
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