REVIEW
Open Access
The exoskeleton expansion: improving
walking and running economy
Gregory S. Sawicki1,2,3*, Owen N. Beck1,2, Inseung Kang1 and Aaron J. Young1,3*
Abstract
Since the early 2000s, researchers have been trying to develop lower-limb exoskeletons that augment human
mobility by reducing the metabolic cost of walking and running versus without a device. In 2013, researchers finally
broke this ‘metabolic cost barrier’. We analyzed the literature through December 2019, and identified 23 studies
that demonstrate exoskeleton designs that improved human walking and running economy beyond capable
without a device. Here, we reviewed these studies and highlighted key innovations and techniques that enabled
these devices to surpass the metabolic cost barrier and steadily improve user walking and running economy from
2013 to nearly 2020. These studies include, physiologically-informed targeting of lower-limb joints; use of off-board
actuators to rapidly prototype exoskeleton controllers; mechatronic designs of both active and passive systems; and
a renewed focus on human-exoskeleton interface design. Lastly, we highlight emerging trends that we anticipate
will further augment wearable-device performance and pose the next grand challenges facing exoskeleton
technology for augmenting human mobility.
Keywords: Wearable robotics, Assistive devices, Metabolic cost, Walk, Run, Energetic, Economy, Augmentation
Background
Exoskeletons to augment human walking and running
economy: previous predictions and recent milestones
The day that people move about their communities with
the assistance of wearable exoskeletons is fast ap-
proaching. A decade ago, Ferris predicted that this day
would happen by 2024 [1] and Herr foresaw a future
where people using exoskeletons to move on natural ter-
rain would be more common than them driving auto-
mobiles on concrete roads [2]. Impressively, Ferris and
Herr put forth these visions prior to the field achieving
the sought-after goal of developing an exoskeleton that
breaks the ‘metabolic cost barrier’. That is, a wearable
assistive device that alters user limb-joint dynamics,
often with the intention of reducing user metabolic cost
during natural level-ground walking and running com-
pared to not using a device. When the goal is to reduce
effort, metabolic cost is the gold-standard for assessing
lower-limb exoskeleton performance since it is an easily
attainable, objective measure of effort, and relates closely
to overall performance within a given gait mode [3, 4].
For example, reducing ‘exoskeleton’ mass improves user
running economy, and in turn running performance [4].
Further, enhanced walking performance is often related
to improved walking economy [3] and quality of life [5,
6]. To augment human walking and running perform-
ance, researchers seriously began attempting to break
the metabolic cost barrier using exoskeletons in the first
decade of this century, shortly after the launch of DAR-
PA’s Exoskeletons for Human Performance Augmenta-
tion program [7–10].
It was not until 2013 that an exoskeleton broke the
metabolic cost barrier [11]. In that year, Malcolm and col-
leagues [11] were the first to break the barrier when they
developed a tethered active ankle exoskeleton that re-
duced their participants’ metabolic cost during walking
(improved walking economy) by 6% (Fig. 1). In the follow-
ing 2 years, both autonomous active [12] and passive [13]
ankle exoskeletons emerged that also improved human
walking economy (Fig. 1). Shortly after those milestones,
Lee and colleagues [14] broke running’s metabolic cost
barrier using a tethered active hip exoskeleton that im-
proved participants’ running economy by 5% (Fig. 1).
Since then, researchers have also developed autonomous
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* Correspondence: gregory.sawicki@me.gatech.edu;
aaron.young@me.gatech.edu
1The George W. Woodruff School of Mechanical Engineering, Georgia
Institute of Technology, Atlanta, GA, USA
Full list of author information is available at the end of the article
Sawicki et al. Journal of NeuroEngineering and Rehabilitation           (2020) 17:25 
https://doi.org/10.1186/s12984-020-00663-9
active [15, 16] and passive [17, 18] exoskeletons that im-
prove human running economy (Fig. 1).
In seven short years, our world went from having
zero exoskeletons that could reduce a person’s meta-
bolic cost during walking or running to boasting
many such devices (Fig. 2). Continued progress to
convert
laboratory-constrained
exoskeletons
to
au-
tonomous systems hints at the possibility that exo-
skeletons
may
soon
expand
their
reach
beyond
college campuses and clinics, and improve walking
and running economy across more real-world venues.
If research and development continues its trajectory,
lower-limb exoskeletons will soon augment human
walking and running during everyday life – hopefully,
fulfilling Ferris’s and Herr’s predictions.
“What a time to be alive” – Aubrey Drake Graham.
Exoskeleton user performance: insights and trends
To highlight the recent growth of exoskeleton technol-
ogy, we compiled peer-reviewed publications that re-
ported that an exoskeleton improved user walking or
running economy versus without using a device through
December 2019. We indexed Web of Science for articles
in the English language that included the following topic:
Fig. 1 Milestones illustrating the advancement of exoskeleton technology. Tethered (blue) and autonomous (red) exoskeletons assisting at the
ankle (circle), knee (triangle), and hip (square) joint to improve healthy, natural walking (left) and running (right) economy versus using no device
are shown
Sawicki et al. Journal of NeuroEngineering and Rehabilitation           (2020) 17:25 
Page 2 of 9
(exoskeleton or exosuit or exotendon or assist robot)
and (metabolic or energetic or economy) and (walking
or running or walk or run). Of the 235 indexed articles,
we only included publications that reported that an exo-
skeleton statistically improved their cohort’s walking
and/or running economy versus an experimental no exo-
skeleton condition. We excluded studies that did not ex-
perimentally compare exoskeleton assisted walking or
running to a no device condition, choosing to focus on
devices that have been shown to break the metabolic
cost barrier in the strictest sense. In total, 23 publica-
tions satisfied our criteria, and six of these articles im-
proved walking economy during “special” conditions:
load carriage [19–21], inclined slope [21, 22], stair ascent
[23], and with enforced long steps [24] (Fig. 2 and
Table 1). We categorized exoskeletons into a special cat-
egory, when researchers increased their participant’s
metabolic cost above natural level-ground locomotion
(e.g. by adding mass to the user’s body), and subse-
quently used an exoskeleton to reduce the penalized
metabolic cost.
Seventeen publications presented improved human
walking and/or running economy using an exoskeleton
versus without using a device during preferred level-
ground conditions: twelve exoskeletons improved walk-
ing economy [11–13, 25–33], four improved running
economy [14, 15, 17, 18], and one improved both walk-
ing and running economy [16] versus using no device
(Fig. 2). These studies demonstrate that exoskeletons
improved net metabolic cost during walking by 3.3 to
19.8% versus using no device. For context, improving
walking economy by 19.8% is equivalent to the change
in metabolic cost due to a person shedding a ~ 25 kg
rucksack while walking [34]. Moreover, four exoskele-
tons improved net metabolic cost during running by 3.9
to 8.0% versus the no device condition (Table 1). Theor-
etically, improving running economy by 8% would en-
able the world’s fastest marathoner to break the current
marathon world record by over 6 min [35] – How about
a 1:50 marathon challenge?
We labeled six studies as “special” due to an added
metabolic penalty placed on the user such as load car-
riage [19–21], enforced unnaturally long steps [24], in-
clined ground slope [21, 22], and/or stair ascent [23]
(Fig. 1). Each of these exoskeletons mitigated the nega-
tive penalty by reducing metabolic cost. Yet, in some
cases [21, 24], the authors also performed a comparison
at level ground walking without an added “special” pen-
alty. In these cases, the exoskeleton did not significantly
mitigate (and may have increased) metabolic cost. For
other “special” cases [19, 22, 23], exoskeletons have
achieved a metabolic cost benefit in other relevant stud-
ies using the same device [12, 26]. However, in such
cases, there were differences in the experimental setup
such as the utilized controller, recruited cohort, and test-
ing conditions.
Despite the popular notion that devices with greater
power density (e.g., tethered exoskeletons with powerful
Fig. 2 The year that each exoskeleton study was published versus the change in net metabolic cost versus walking or running without using the
respective device. Red indicates autonomous and blue indicates a tethered exoskeletons. Different symbols indicate the leg joint(s) that each
device directly targets. Asterisk indicates special case and cross indicates a passive exoskeleton
Sawicki et al. Journal of NeuroEngineering and Rehabilitation           (2020) 17:25 
Page 3 of 9
Table 1 Detailed device specifications for exoskeletons that improved healthy, natural walking, and/or running economy versus
using no device
Number LeadAuthor Year
Metabolic
Reduction
(%)
Sample
Size
Target
Joint(s)
Auto
/Tethered
Active
/Passive
Walk
/Run
Speed
(m/s)
Mode
Device
Mass (kg)
Note
1
G Sawicki
2009
14
9
Ankle
Tethered
Active
Walk
1.25
Level
Ground
2.36
Long Step Lengths
2
P Malcolm
2013
6
8
Ankle
Tethered
Active
Walk
1.38
Level
Ground
1.52
3
L Mooney
2014a
8
7
Ankle
Autonomous Active
Walk
1.5
Level
Ground
4
Load Carry (23 kg)
4
L Mooney
2014b 10
7
Ankle
Autonomous Active
Walk
1.4
Level
Ground
3.6
5
S Collins
2015
7.2
9
Ankle
Autonomous Passive
Walk
1.25
Level
Ground
0.91
6
L Mooney
2016
11
6
Ankle
Autonomous Active
Walk
1.4
Level
Ground
3.6
7
K Seo
2016
13.2
5
Hip
Autonomous Active
Walk
1.17
Level
Ground
2.8
8
G Lee
2017
5.4
8
Hip
Tethered
Active
Run
2.5
Level
Ground
0.81
9
S Galle
2017
12
10
Ankle
Tethered
Active
Walk
1.25
Level
Ground
1.78
10
Y Lee
2017
13.2
5
Hip
Autonomous Active
Walk
1.14
Level
Ground
2.6
11
K Seo
2017
15.5
5
Hip
Autonomous Active
Walk
1.17
Inclined
Slope
2.4
5% grade
12
H Lee
2017
7
30
Hip
Autonomous Active
Walk
1.1
Level
Ground
2.8
Elderly
13
R Nasiri
2018
8
10
Hip
Autonomous Passive
Run
2.5
Level
Ground
1.8
14
S Lee
2018
14.9
7
Hip,
Ankle
Autonomous Active
Walk
1.5
Level
Ground
9.3
Load Carry (6.8 kg)
15
Y Ding
2018
17.4
8
Hip
Tethered
Active
Walk
1.25
Level
Ground
1.37
16
J Kim
2018
3.9
8
Hip
Autonomous Active
Run
2.5
Level
Ground
4.7
Hybrid System
17
D Kim
2018
10.16
15
Hip
Autonomous Active
Walk
N/A
Stair
Ascent
2.8
Elderly/128 Steps
18
F Panizzolo
2019
3.3
9
Hip
Autonomous Passive
Walk
1.1
Level
Ground
0.65
Elderly
19
M MacLean 2019
4.2
4
Knee
Autonomous Active
Walk
0.5
Inclined
Slope
8.4
Load Carry (18.1 kg)
/ 15 deg incline
20
C Simpson
2019
6.4
12
Hip
Autonomous Passive
Run
2.67
Level
Ground
N/A
Ankle Attachment
21
J Kim
2019
9.3
9
Hip
Autonomous Active
Walk
1.5
Level
Ground
5
Hybrid System
22
J Kim
2019
4
9
Hip
Autonomous Active
Run
2.5
Level
Ground
5
Hybrid System
23
B Lim
2019
19.8
6
Hip
Autonomous Active
Walk
1.11
Level
Ground
2.1
24
C Khazoom
2019
5.6
8
Ankle
Tethered
Active
Walk
1.4
Level
Ground
6.2
Sawicki et al. Journal of NeuroEngineering and Rehabilitation           (2020) 17:25 
Page 4 of 9
off-board motors and lightweight interfaces) would re-
duce user metabolic cost beyond that capable by autono-
mous
devices,
to
date
tethered
systems
have
not
improved user walking/running economy beyond that of
autonomous systems (t-test: p = 0.90) (Fig. 2). Namely,
tethered exoskeletons have improved user net metabolic
cost during walking by 5.4 to 17.4% and autonomous
exoskeletons have improved net metabolic cost during
walking by 3.3 to 19.8%. These data are from a variety of
devices (Table 1), walking speeds, and control systems,
and thus more rigorous comparisons between autono-
mous and tethered systems may reveal a more stark per-
formance benefit of tethered systems due to their
inherently smaller added mass penalty.
Even though distal leg muscles are thought to be more
economical/efficient than proximal leg muscles [36, 37],
ankle exoskeletons broke the metabolic cost barrier before
hip exoskeletons. Perhaps that is because researchers ini-
tially targeted the ankles because they yield the greatest
positive mechanical power output of any joint [37]. Not-
ably, only one knee exoskeleton has improved walking
economy [21] (Fig. 2). Finally, hip exoskeletons (17.4%
metabolic reduction for a tethered device and 19.8% for an
autonomous device) have numerically improved metabolic
cost by more than ankle exoskeletons (12% metabolic re-
duction for a tethered case and 11% for an autonomous
device), perhaps due to the physiological differences be-
tween ankle and hip morphology [37, 38] and/or due to
the location of the device’s added mass [39].
A closer examination of the subset of exoskeletons
that have yielded the greatest metabolic benefit provides
insight into the factors that may maximize users’ benefits
with future devices. One emerging factor is the exoskel-
eton controller. There are numerous methods to com-
mand [40] and control exoskeleton torque profiles. For
example, myoelectric controllers depend on the user’s
muscle activity [41, 42] and impedance controllers de-
pend on the user’s joint kinematics [43]. Time-based
controllers do not take the state of the user as direct in-
put, and only depend on the resolution offered by the
chosen torque versus time parameterization [27, 30, 44].
Recent exoskeleton studies indicate that both magnitude
[45, 46] and perhaps more importantly, timing of assist-
ance [11, 47, 48], affect user metabolism. Additionally,
time-based controllers have the flexibility to generate a
generalized set of assistive torque patterns that can be
optimized on the fly and considerably improve walking
and running economy over zero-torque conditions [30,
44]. Interestingly, the optimal exoskeleton torque pat-
terns that emerge do not correspond to physiological
torques in either their timing or magnitude [14, 44]. But,
at least at the ankle, getting the timing right seems para-
mount, as data from optimized exoskeleton torque pat-
terns
show
lower
variability
in
the
timing
versus
magnitude of the peak torque across many users [44]. Fi-
nally, regarding the magnitude of exoskeleton torque
and the net mechanical energy transfer from the device
to the user, more is not always better with respect to im-
proving user locomotion economy [13, 27, 44, 46].
Leading approaches and technologies for
advancing exoskeletons
Exoskeleton testbeds enable systematic, high throughput
studies on human physiological response
Tethered exoskeleton testbeds have accelerated device de-
velopment. In the first decade of the twenty-first century,
most exoskeletons were portable, but also cumbersome
and limited natural human movement. In addition, these
devices were typically designed for one-off, proof of con-
cept demonstrations; not systematic, high-throughput re-
search [49–52]. As researchers began focusing on studies
that aimed to understand the user’s physiological response
to exoskeleton assistance, a key innovation emerged - the
laboratory-based exoskeleton testbed. Rather than placing
actuators on the exoskeleton’s end-effector, researchers
began placing them off-board and attached them through
tethers (e.g., air hoses and Bowden cables) to streamlined
exoskeleton end-effectors [45, 53, 54]. This approach en-
abled researchers to conduct high throughput, systematic
studies during treadmill walking and running to determine
optimal exoskeleton assistance parameters (e.g., timing
and magnitude of mechanical power delivery [27, 55]) for
improving walking and running economy. Furthermore,
the high-performance motors on recent tethered exoskel-
eton testbeds have relatively high torque control band-
width that can be leveraged to render the dynamics of
existing or novel design concepts [43, 56]. Testing mul-
tiple concepts prior to the final device development could
enable researchers to quickly diagnose the independent ef-
fects of design parameters on current products and test
novel ideas [57]. Thus, we reason that exoskeleton test-
beds have progressed exoskeleton technology by enabling
researchers to optimize a high number of device parame-
ters [58], test new ideas, and then iterate designs without
having to build one-off prototypes.
Embedding ‘smart mechanics’ into passive exoskeletons
provides an alternative to fully powered designs
Laboratory-based exoskeletons are moving into the real-
world through the use of small, transportable energy
supplies [59] and/or by harvesting mechanical energy to
power the device [60]. Despite these improvements, an-
other way to circumnavigate the burden of lugging
around bulky energy sources is by developing passive
exoskeletons [13, 17, 18, 31]. Passive exoskeletons have
been able to assist the user by storing and subsequently
returning mechanical energy to the user without inject-
ing net positive mechanical work. Passive exoskeletons
Sawicki et al. Journal of NeuroEngineering and Rehabilitation           (2020) 17:25 
Page 5 of 9
are typically cheaper and lighter than active devices (e.g.,
Collins et al.’s ankle exoskeleton is 400 g [13]) and, like
active devices, are hypothesized to primarily improve
walking and running economy by reducing active muscle
volume [61]. However, due to their simplified designs,
passive exoskeletons are in some ways less adaptable
than powered devices. Passive devices can only offer
fixed mechanical properties that are at best only switch-
able between locomotion bouts. Thus, while passive sys-
tems may be adequate for providing assistance during
stereotyped locomotion tasks such as running on a track
or hiking downhill at fixed speed, they may not be able
to handle variable conditions. On the other hand, active
devices offer the opportunity to apply any generic
torque-time profile, but require bulky motors and/or
gears that need a significant source of power to do so.
Thus, combining features from active and passive exo-
skeletons to create a new class of pseudo-passive (or
semi-active) devices may yield a promising future direc-
tion for exoskeleton technology [59]. For example, rather
than continuously modulating the assistance torque pro-
file, a pseudo-passive device might inject small amounts
of power to change the mechanical properties of an
underlying passive structure during periods when it is
unloaded [62]. The pseudo-passive approach likely bene-
fits from the streamlined structural design (e.g., small
motors)
and
adaptability
that
requires
only
small
amounts of energy input (e.g., small batteries).
Providing comfort at the human-exoskeleton interface
Regardless of active or passive exoskeleton design, re-
searchers struggle to effectively and comfortably inter-
face exoskeletons to the human body [63]. That is
primarily due to the human body having multiple de-
grees of freedom, deforming tissues, and sensitive points
of pressure. Accordingly, many researchers utilize cus-
tom orthotic fabrication techniques [46, 64, 65], and/or
malleable textiles (commonly referred to as exo-suits)
[16, 66–68] to tackle this challenge. Textile-based exo-
skeletons may be superior to traditional rigid exoskeletons
due to their lower mass, improved comfort, fewer kine-
matic restrictions, and better translation to practical-use
[16, 67, 68]. Reaffirming soft technology, the tethered exo-
skeleton that best improves walking economy versus not
using a device is currently an exoskeleton with a soft, mal-
leable user-device interface [67] (Fig. 2).
Exoskeleton controllers using artificial intelligence and on-
line optimization to adapt to both user and environment
may facilitate the transition to ‘real-world’ functionality
Researchers are also developing smart controllers that
constantly update exoskeleton characteristics to optimize
user walking and running economy. This is exemplified by
Zhang and colleagues [44], who developed a controller
that rapidly estimates metabolic profiles and adjusts ankle
exoskeleton torque profiles to optimize human walking
and running economy. We foresee smart controllers enab-
ling exoskeletons to move beyond conventional fixed as-
sistance parameters, and steering user physiology in-a-
closed-loop with the device to maintain optimal exoskel-
eton assistance across conditions [30, 69]. Since measuring
metabolic cost throughout everyday life is unrealistic, fu-
ture exoskeletons may incorporate embedded wearable
sensors (e.g., electromyography surface electrodes, pulse
oximetry
units,
and/or
low-profile
ultrasonography
probes) that inform the controller of the user’s current
physiological state [70, 71] and thereby enable continuous
optimizing of device assistance [20, 72, 73] to minimize
the user’s estimated metabolic cost.
At a high level of control, researchers are using tech-
niques to detect user intent, environmental parameters,
and optimize exoskeleton assistance across multiple
tasks [15, 16, 68, 74, 75]. An early version of this tech-
niques paradigm was implementing proportional myo-
electric control into exoskeletons [76–78]. This strategy
directly modulates exoskeleton torque based on the tim-
ing and magnitude of a targeted muscle’s activity, which
can adapt the device to the users changing biomechan-
ics. However, this strategy has yielded mixed results [42,
79, 80] and is challenging to effectively use due to quick
adaptations that occur to accommodate various tasks as
well as slower changes that occur due to learning the de-
vice [41]. Scientists have made exciting advances using
machine learning and artificial intelligence techniques to
fuse information from both sensors on the user and de-
vice to better merge the user and exoskeleton [81, 82],
but these techniques have not yet been commercially
translated to exoskeleton technology to the authors’
knowledge. These strategies have the potential to enable
exoskeletons to discern user locomotion states (such as
running, walking, descending ramps, and ascending
stairs) and alter device parameters to meet the respective
task demands.
Conclusion
Closing remarks and vision for the future of exoskeleton
technology
In the near-term, we predict that the exoskeleton expan-
sion will break researchers out of laboratory confine-
ment. Doing so will enable studies that directly address
how exoskeleton-assistance affects real-world walking
and running performance without relying on extrapo-
lated laboratory-based findings. By escaping the labora-
tory, we expect that exoskeleton technology will expand
beyond improving human walking and running economy
over the next decade and begin optimizing other aspects
of locomotor performance that influence day-to-day mo-
bility in natural environments. To list a few grand-
Sawicki et al. Journal of NeuroEngineering and Rehabilitation           (2020) 17:25 
Page 6 of 9
challenges, exoskeletons may begin to augment user sta-
bility, agility, and robustness of gait. For example, exo-
skeletons may make users,
· More stable by modulating the sensorimotor response
of their neuromuscular system to perturbations [83–85].
· More agile and faster by increasing the relative force
capacity of their muscles [86].
· More robust by dissipating mechanical energy to
prevent injury during high impact activities like rapid
cutting maneuvers or falling from extreme heights [87].
To make these leaps, engineers will need to continue to
improve exoskeleton technology, physiologists will need
to refine the evaluation of human performance, clinicians
will need to consider how exoskeletons can further re-
habilitation interventions, psychologists will need to better
understand how user’s interact with and embody exoskel-
etons, designers will need to account for exoskeletons in
space planning, and healthcare professionals may need to
update their exercise recommendations to account for the
use of exoskeletons. Combined, these efforts will help es-
tablish a ‘map’ that can be continuously updated to help
navigate the interaction between human, machine, and
environment. Such guidelines will set the stage for exo-
skeletons that operate in symbiosis with the user to blur
lines between human and machine. Closing the loop be-
tween exoskeleton hardware, software, and the user’s bio-
logical systems (e.g., both musculoskeletal and neural
tissues) will enable a new class of devices capable of steer-
ing human neuromechanical structure and function over
both short and long timescales during walking and run-
ning. On the shortest of time scales, exoskeletons that
have access to body state information have the potential
to modify sensory feedback from mechanoreceptors and
augment dynamic balance. On the longest of timescales,
exoskeletons that have access to biomarkers indicating tis-
sue degradation [88] could modify external loads to shape
the material properties of connective tissues and maintain
homeostasis.
Until then, we focus our attention on the ability of exo-
skeletons to improve human walking and running econ-
omy. So far, 17 studies have reported that exoskeletons
improve natural human walking and running economy
(Fig. 2). As these devices evolve and become more avail-
able for public use, they will not only continue to improve
walking and running economy of young adults, but they
will also augment elite athlete performance, allow older
adults to keep up with their kinfolk, enable people with
disability to outpace their peers, and take explorers deeper
into the wilderness.
Acknowledgements
None.
Authors’ contributions
All authors contributed to writing the manuscript. G. Sawicki and A. Young
jointly conceived of the review paper idea, extracted the trends, and
determined the leading approaches and technologies. O. Beck and I. Kang
performed a literature search to benchmark progress in exoskeletons vs. time
to improve the economy of human locomotion. They logged all the studies
and categorized them by joint and gait mode. All authors drafted, edited,
and approved the final manuscript.
Funding
This work was funded in part by NSF National Robotics Initiative (award #
1830215) to A.J.Y., U.S. Army Natick Soldier Research, Development and
Engineering Center (W911QY18C0140) to G.S.S, and an NIH National Institute
on Aging fellowship award (F32AG063460) to O.N.B. The content is solely the
responsibility of the authors and does not necessarily represent the official
views of the funding agencies listed.
Availability of data and materials
Not applicable.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1The George W. Woodruff School of Mechanical Engineering, Georgia
Institute of Technology, Atlanta, GA, USA. 2School of Biological Sciences,
Georgia Institute of Technology, Atlanta, GA, USA. 3Institute for Robotics and
Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA.
Received: 5 August 2019 Accepted: 13 February 2020
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The exoskeleton expansion: improving walking and running economy.
Sawicki, Gregory S,Beck, Owen N,Kang, Inseung,Young, Aaron J