sciencemag.org SCIENCE1230 23 JUNE 2017 • VOL 356 ISSUE 6344
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F ast exoskeleton optimization
PERSPECTIVES
An algorithm optimizes exoskeleton walking assistance in 1 hour
By Philippe Malcolm,1 Samuel Galle,2
Dirk De Clercq2
Identifying the optimal pattern of assis-
tive torque provided by an exoskeleton
over the course of the person’s walking
stride is challenging. Engineers have
been developing wearable devices to
reduce the metabolic cost of walking
for more than a century, but only in the
past 4 years have groups succeeded in this,
using ankle exoskeletons (1–3). Brute-force
approaches can test various timing and
magnitude settings of the torque pattern
and identify the settings that produce the
largest reduction in metabolic cost (1, 3–6).
However, obtaining reliable metabolic cost
data requires averaging multiple minutes
of breath data, which in turn limits the
number of settings that can be tested. On
page 1280 of this issue, Zhang et al. (7) de-
scribe an algorithm that optimizes the en-
tire exoskeleton torque pattern in a 1-hour
iterative process with real-time metabolic
cost estimations. This smart human-in-
the-loop algorithm identified an optimal
pattern for each participant and resulted
in an average reduction of 24% compared
1Department of Biomechanics, Center for Research in Human Movement Variability, University of Nebraska Omaha, NE 68182, USA. 2Department of Movement and Sports Sciences, Ghent University, B 9000 Ghent, Belgium.Email: [email protected]
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to participants walking with the exoskel-
eton powered off.
This reduction is slightly better than the
best results from other groups (4, 6), and
it is impressive considering that Zhang et
al. assisted only one leg. Also, after the op-
timization phase, the average reduction in
metabolic cost with a standardized (non-
optimized) torque pattern was greater than
in a previous experiment with the same
pattern and exoskeleton (5). The authors
suggest that the human-in-the-loop algo-
rithm might have facilitated motor learn-
ing by exposing participants to a wider
variety of torque patterns (see the figure).
Indeed, since the 1970s, scientists showed
that variable practice improves skill learning
(8). This concept challenged the then-pre-
vailing view that practicing should happen
under constant conditions. Variable practice
is now applied in sports, physical therapy,
and learning of skills in professions. The re-
duction in metabolic cost achieved by Zhang
et al. seems to result from a combination of
effective torque pattern optimization and fa-
cilitation of motor learning by exposure to a
wide variety of torque patterns.
It is possible that motor learning could
be further improved by increasing practice
variability. For upper-limb rehabilitation,
robotic devices that amplify movement
errors improve training in stroke patients
(9). To increase variability in locomotion,
non-steady-state walking conditions could
provide a learning environment that is
both more realistic and variable. Walking
during daily life happens on uneven ter-
rain during short bouts (10) with frequent
changes in speed. Human-in-the-loop op-
timization during non-steady-state walk-
ing would be challenging for the current
algorithm designed for cyclic gaits. It may
be possible that walking with an exoskele-
ton could be practiced separately from the
torque pattern optimization, which would
require a controller that simply provides a
variety of torque patterns. Finding the best
method for learning to walk with exoskel-
etons will require studies with different
training modalities with participants who
start from an untrained state.
Given the ability to improve metabolic
economy, improving performance could
become a next objective. Preferred walking
speed could be used as an objective for a
human-in-the-loop algorithm. Ten years
ago, Norris et al. (11) were able to increase
preferred walking speed with an ankle
exoskeleton. Such an approach could ben-
efit patients with reduced exercise capacity
(e.g., pulmonary impairment).
The study by Zhang et al. has not only
demonstrated a solution for optimizing
and individualizing exoskeleton torque
patterns but has also led to new questions
about motor learning. Given its generaliz-
ability, online optimization should have
multiple applications for the development
of wearable robotics. For human move-
ment science in general, human-in-the-
loop optimization could allow new types of
experiments where relations between gait
parameters are investigated in real time
rather than by testing protocols composed
of a fixed set of conditions. j
REFERENCES AND NOTES
1. P. Malcolm, W. Derave, S. Galle, D. De Clercq, PLOS ONE 8, e56137 (2013).
2. L. M. Mooney, E. J. Rouse, H. M. Herr, J. Neuroeng. Rehabil. 11, 80 (2014).
3. S. H. Collins, M. B. Wiggin, G. S. Sawicki, Nature 522, 212 (2015).
4. B. T. Quinlivan et al., Sci. Robot. 2, eaah4416 (2017). 5. R. W. Jackson, S. H. Collins, J. Appl. Physiol. 119, 541 (2015). 6. S. Galle, P. Malcolm, S. H. Collins, D. De Clercq, J. Neuroeng.
Rehabil. 14, 35 (2017). 7. J. Zhang et al., Science 356, 1280 (2017). 8. R. A. Schmidt, T. D. Lee, Motor Learning and Performance:
From Principles to Application (Human Kinetics, Champaign, IL, ed. 5, 2013).
9. J. L. Patton, M. E. Stoykov, M. Kovic, F. A. Mussa-Ivaldi, Exp. Brain Res. 168, 368 (2006).
10. M. S. Orendurff, J. A. Schoen, G. C. Bernatz, A. D. Segal, G. K. Klute, J. Rehabil. Res. Dev. 45, 1077 (2008).
11. J. A. Norris, K. P. Granata, M. R. Mitros, E. M. Byrne, A. P. Marsh, Gait Posture 25, 620 (2007).
ACKNOWLEDGMENTS
We thank our colleagues for editorial suggestions. This work was supported by NIH (P20GM109090).
10.1126/science.aan5367
23 JUNE 2017 • VOL 356 ISSUE 6344 1231SCIENCE sciencemag.org
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Lab-based human-in-the-loop optimization from
Zhang et al. will inform the control of other wearable
robots that can be used for clinical populations and
outdoor walking, such as the Harvard Biodesign Lab
exosuit shown here. Outdoor walking could provide
a rich variable-practice environment that promotes
human motor learning.
Torque patterns from Zhang et al. Torque patterns from other studies
cover a smaller range
Time (% stride period)
0 20 40 60 80 100
Zhang et al. (7)Malcolm et al. (1)Collins et al. (3)Quinlivan et al. (4)Jackson et al. (5)Galle et al. (6)
0 20 40 60 80 100
0
0.2
0.4
0.6
0.8
1.0
To
rqu
e (N
m k
g-1
)
Range of possible torque patterns
Examples of possible torque patterns
Expanding the range of torque patternsThe range of possible torque patterns delivered by an exoskeleton that provides walking assistance, as
described by Zhang et al. (left), is greater than that in previous studies (right). This wider variety of actuation
patterns could have facilitated motor learning and contributed to large metabolic cost reductions.
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Fast exoskeleton optimizationPhilippe Malcolm, Samuel Galle and Dirk De Clercq
DOI: 10.1126/science.aan5367 (6344), 1230-1231.356Science
ARTICLE TOOLS http://science.sciencemag.org/content/356/6344/1230
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