Analysing human-exoskeleton interaction: on the human adaptation to modified
S. Bastidea,b, N. Vignaisa,b*, F. Geffardc, and B. Berreta,b,d
aCIAMS, Univ. Paris-Sud, Université Paris-Saclay, 91405 Orsay Cedex, France; bCIAMS, Université d'Orléans, 45067,
Orléans, France; cCEA, LIST, Interactive Robotics Laboratory, 91191 Gif-sur-Yvette, France; dInstitut Universitaire de
Keywords: gravito-inertial dynamics; motor control; adaptation; exoskeleton
Robotic exoskeletons are promising devices to assist
operators in the industry sector and prevent
musculoskeletal disorders. With such devices, typical
load lifting tasks can be performed with less effort for
the operator. She/he would just have to guide the
motion of the exoskeleton, usually set in “transparent”
mode. In the most basic and robust implementation of
this mode, the exoskeleton compensates its own weight
(plus the weight of any attached load) and frictions.
From a human-centered point of view, such an
interaction leads to a quite unfamiliar dynamic
situation. Indeed, in daily life, our central nervous
system (CNS) has to deal with both static and dynamic
forces acting at the joints. During object manipulation,
gravitational torque (GT) scales with inertial torques
(IT) because gravitational and inertial masses are the
same. In contrast, with a “transparent” exoskeleton,
this implicit relationship is altered because the operator
feels the additional inertia of the robot (and potential
additional loads) without any associated weight
increment. Given that GT and IT are major constitutive
components of upper-limb dynamics, understanding
how humans adapt to such unfamiliar perturbations in
practice is critical. In motor control, adaptation to a
weight load at the hand (incrementing GT and IT
together) is quasi-instantaneous as it corresponds to a
familiar dynamic situation (Bock et al., 1990).
However, adaptation to unfamiliar dynamic conditions
can take tens to hundreds of trials (Ingram et al., 2011).
Previous studies on weight load perturbations (Bock et
al., 1992), force field adaptation (Kurtzer et al., 2005),
grasping forces (Zatsiorsky et al., 2005) or
weightlessness (Gaveau et al., 2016) suggest that the
CNS has internal models dissociating GT and IT. This
could allow conforming efficiently and rapidly to
novel gravito-inertial dynamics. However, moving
objects with inertia but no apparent weight, as in a
human/exoskeleton interaction, is quite uncommon on
earth and it may well disturb human motor control
(Bastide et al,. 2018). Given the unfamiliarity of such
a perturbation, it is therefore reasonable to expect that
the CNS would need few trials to adapt to its motor
controller. Thus, the aim of this study is to analyze the
adaptation to a “transparent” upper-limb exoskeleton
during simple elbow flexion/extension movements,
given that the exoskeleton induces a relative
modification of GT and IT during the task.
Twenty-one healthy right-handed young adults (7
females, 14 males) participated to this study. Mean
age, height and weight were 24.9±4.7, 175.7±8.4 cm
and 68.9±11.1 kg, respectively. Written informed
consent was obtained from each participant in the
study as required by the Helsinki declaration. The local
ethical committee for research (Univ. Paris Saclay)
approved the experimental protocol.
The ABLE upper-limb exoskeleton was used in this
experiment (Garrec et al., 2008). The mass and the
length to the center of mass of its forearm segment
were 2.32 kg and 11 cm. The elbow joint position was
recorded from the exoskeleton sensors. We also
recorded surface electromyographic (EMG) data of
two flexors muscles (biceps brachial, brachioradialis)
and two extensors (triceps brachial lateral head, triceps
brachial long head). EMG signals were collected using
wireless sensors (Biometrics Ltd, UK). Both kinematic
and EMG signals were sampled at 1000 Hz.
Participants sat straight with their back leaning against
the rigid base of the exoskeleton. The participant’s
right forearm was attached to the exoskeleton at wrist
level. Alignment of elbow centers of rotation of the
participant and the exoskeleton was adjusted by
calibrating the height of the whole robotic device.
Participants were asked to perform point-to-point
reaching movements between two lighting targets
(LED), involving 60° elbow flexion/extension
movements. Exoskeleton joints other than the elbow
were frozen to ensure that movements could only be
performed with the forearm. Participants were
instructed to point in the direction of the target that lit
up. The target remained on for one second before it
turned off. To get discrete movements, a random pause
duration of 1 to 2.5 seconds was displayed between
each movement. Overall, each participant performed
50 flexions and 50 extensions of the elbow. The
exoskeleton control law was set to compensate its own
frictions and GT (but not its IT).
2.4 Data processing
Angular velocity and acceleration were obtained by
numerical differentiation of the recorded angular
positions. The movement was considered effective
when the elbow angular velocity exceeded 5% of the
maximum. Flexors and extensors muscles were
respectively grouped and averaged. Muscle activations
were expressed as a percentage of the maximal data
obtained during the experiment. The last 40 trials have
been used to define the 95% Confidence Interval of the
3. Results and discussion
Figure 1: Mean duration, velocity, maximal
acceleration and maximal elbow flexor activity (±SE)
during elbow flexion movements for all participants.
Red lines represent data exponential fitting.
Duration of the three firsts movements were
significatively different from the subsequent ones
according to the predefined CIP (see figure 1). The
mean velocity and the mean maximal acceleration are
also significantly lower for the three firsts flexions.
Same observations were made for extensions. Thus six
full movements, i.e. flexion and extension, were
necessary to conform to the new gravito-inertial
dynamics situation. Moreover, a higher overshoot
(6.83±3.4°) was found only for the very first flexion
compared to overshoot CIP ([1.6°, 2°]), showing a
rapid adaptation process to achieve the task.
3.2 Muscle activity
Agonists muscular activations followed the same
trend, with only the very first flexion being out of the
maximal activation CIP (see figure 1). Concerning
extensors, maximal activation (33±15.2 %) was also
out of the CIP ([51.2%, 53.6%]) only for the first
extension, suggesting that participants underestimated
the inertial mass being manipulated during the very
first movement of flexion/extension (likely because it
could not be inferred from the exoskeleton weight).
This study aimed at analyzing the human adaptation to
modified gravito-inertial dynamics induced by an
upper-limb exoskeleton. Participants needed one to
three flexion/extension movements to adapt to the
“transparent” mode of the exoskeleton. Thus, our
initial hypothesis was confirmed, i.e. adaptation to a
transparent exoskeleton is rapid but not as immediate
as in classical load lifting tasks. Future work will
investigate human adaptations to other control modes,
e.g. compensation of the user’s arm weight.
Bock O. 1992. The characteristics of arm movements
executed in unusual force environments. ASR.
Bastide S, Vignais N, Geffard F, Berret B. 2018.
Interacting with a “Transparent” Upper-Limb
Exoskeleton: A Human Motor Control Approach.
Proc IEEE IROS. 4661-4666.
Kurtzer I, DiZio P, Lackner JR. 2005. Adaptation to a
novel multi-force environment. Exp. Brain Res.,
Zatsiorsky VM, Gao F, Latash ML. 2005. Motor
control goes beyond physics: differential effects of
gravity and inertia on finger forces during
manipulation of hand-held objects. Exp. Brain Res.
Gaveau J, Berret B, Angelaki DE, Papaxanthis C.
2016. Direction-dependent arm kinematics reveal
optimal integration of gravity cues. eLife, 5.
Ingram JN, Howard IS, Flanagan JR, Wolpert DM.
2011. A Single-Rate Context-Dependent Learning
Process Underlies Rapid Adaptation to Familiar
Object Dynamics. PLoS Comput. Biol.
7(9) :e1002196, Sept.
*Corresponding author. Email: email@example.com