Instrumentation & Measurement Magazine 25-9 - 29

technology is used to eliminate the noise. Mathematical tools,
relying on statistics and regression, are usually needed to derive
a more detailed relationship between the limbs. Next, the
gecko- mimicking robot model is created and simulated in motion
analysis software to assess the feasibility and effect of the
bio-inspired locomotion approach, as well as the intra- and
inter-limb coordination performance. Finally, the experiment
is then carried out on a physical robot to ensure that it is available
and feasible.
Observation of Animal Movement and
Equipment
Locomotion involves the harmonious activity of an animal's
entire body, especially depending on the coordinated action
of musculoskeletal system, nervous system and the sensory
organs [12]. Spatio-temporal gait characteristics and patterns
of locomotive cycles are the collective results of the intrinsic
properties and motion system of each animal [13]. Locomotion
observation is a wide topic [14], and the expected outcomes of
the observation should be considered before the experiment,
so the observation equipment can be decided.
For locomotion experiments, a variety of motion capture
techniques and technologies have been created [15]. Commercial
motion capture equipment is easy to use and comes
with user-friendly software like OptiTrack (Corvallis, Oregon,
USA), Vicon (Oxford, England, UK), NOKOV (Beijing,
China) and others. The positions of the markers are tracked
and captured by optical cameras, and then the trajectories can
be generated directly from the software by placing them on
the predetermined places in preparation. However, a special
setup is required because the markers are difficult to stick to a
small animal's body. A high-speed camera can clarify what is
otherwise fuzzy or too fast to see and has enabled significant
resolution of movement.
For the locomotion observation of a gecko in this project,
the specialized motion capture device was built, as shown in
Fig. 2 [16]. In order to get the curves of joint angles and feet
tips, the conceived model of the gecko was based on the natural
characteristics of the gecko (Fig. 2a). The joint angles in the
concerned projection planes are defined as referred to the conceived
robot. The femorotibial angle (α) is the angle between
femur and tibia, and is always positive. The swing angle (β) is
calculated as the projection of the angle between femur and a
plane through the coxa parallel with the coronal plane in the
body plane. Swing angles in front of the parallel plane are considered
positive, while behind this plane are negative. The
lifting angle (γ) is defined as the projection of the angle between
femur and a plane through the coxa parallel with the
body plane in the coronal plane. Lifting angles are considered
positive before the parallel plane, and negative behind this
plane. The experimental setup is shown in Fig. 2b, in which a
tunnel is made up of a long soft board for the gecko's locomotion,
and one long mirror on each side is mounted along the
board with a 45 degree angle relative to the track, and thus the
two lateral sides of the gecko are captured during movement
from the mirrors, as shown in Fig. 2c. To clarify the positions in
the captured photos, the joints of the gecko were highlighted
by fluorescent markers in advance.
The positions of body markers in the photographs are extracted
in the form of pixels. The positions of the points are
then translated to real positions using the pixel-to-real-length
scale. The three-dimensional positions of each marker are
computed by merging the normal and lateral photographs, as
are the trajectories of the foot tips relative to the corresponding
hip joints. The values of the joint angles can then be determined
using geometric functions.
For the motion tracking technique with marker, the detection
and recognition of the markers on the motion target were
affected by the size, color, quantity, position and so on [17],
[18]. For complex motion capture of the small size animals
with soft or rigid-flexible coupling structure, positions of the
marker were easily overlapped with each other because of the
special warping and bending deformation of the limbs. The
extracting of the behavior for further analysis can be highly
time-consuming. The location and number of the markers are
intrusive and must be determined a priori. Machine learning
has been developed for the markerless pose estimation. Alexander
Mathis et al. [19] provide an open-source software
Fig. 2. The locomotion observation system for gecko. (a) The predefined angles; (b) Observation equipment; (c) Gecko photograph captured while moving.
(Adapted from [16] with permission ©2009 Springer Nature.)
December 2022
IEEE Instrumentation & Measurement Magazine
29

Instrumentation & Measurement Magazine 25-9

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