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2006). The adaptation of cyclical activation, has
demonstrated proper results at self-preferred constant
speeds. The next proposed method is an improvement
intended to provide the required dynamicaladaptation
to changes of step frequency/length by the user.
1.3 Bipedal walking with Central
Pattern Generators (CPGs):
Simulation
It has been demonstrated previously how the use of
the dynamical systems paradigm can realize a walk-
ing behavior in robotic walking platforms (Veskos
and Demiris, 2006). The neural architecture has
demonstrated successful operation in swinging and
planar walking in a bipedal platform, incorporating
van der Pol oscilators as generators of motor com-
mands.
Medium and short term application of a walking
real-time controller for the mentioned application sce-
narios, ought include mechanisms that provide adapt-
ability and stable response to variations of frequency
in the feedback signals, can led to an approach of co-
operative development with the user/environment. In
the following, the analysis of the response of the pro-
posed hybrid controller to variations in gait frequency
is evaluated with real data measured with the orthotic
walking platform.
2 METHODS
2.1 Gait Patterns with Knee Joint
Compensator
Subjects wearing an exoskeleton, need to adapt their
walking strategy to drive the system to successfully
switch between two knee spring damper configura-
tions. During the entrainment of the subject with the
controllable exoskeleton it is necessary to reach a cer-
tain ankle dorsiflexion angle which is variable during
normal gait. Although this angle is adjustable, sub-
jects change their gait pattern until they learn to use
the exoskeleton. The learning process (which can be
seen as an adaptation) in the use of the controllable
exoskeleton has been previously studied in (Forner-
Cordero et al., 2006). In order to obtain sampled
data of different gait speeds, experimental trials with
a healthy subject have been conducted after the adap-
tation process, consisting in walking back and forth
along a 10 meter path, with definition of the step
length with marks on the floor and the gait speed by
means of a metronome, and systematic adjustments
of the cable mechanism to provide a comfortable gait
pattern (see table 1). The gait velocity and step length
variations were defined according to average values
taken from Perry, (Perry, 1999), consisting in feasi-
ble combinations of 100%, 70%, 60% and 50%. Rate
gyroscopes fixed at the shank and leg segments of the
external device were used to measure rotational veloc-
ities along the sagittal plane. Motions of interest oc-
cur at normal (2.6 km/h) and low (2 km/h) gait speeds,
and therefore, signals outside the band frequency re-
lated to gait kinematics (0.3–20 Hz), are rejected from
the sensor outputs with -3 dB low pass filters, refer
(Moreno et al., 2006) to for details. A precision angu-
lar position sensor was fixed at the knee joint to track
the knee joint angle in the sagittal plane. A resistive
pressure sensor (5 mm in diameter active area, 0.30
mm thickness) is used to monitor the activation status
of the knee actuator.
Collection of input/ouput data is utilized to gener-
ate training and checking data sets, of both multiple
speed trials, and constant speed separated trials.
2.2 Validation Model
A robust Model describing the dynamics of the knee-
orthotic hinge system during cyclic walking condi-
tions can be used as a reference to analyze the per-
formance of the advanced control system. We pro-
pose the identification of the model the activation pat-
terns provided by the cable driven exoskeleton, with
time-series of kinematic data. A broadly used signal
processing paradigm is the state-space model. De-
fined by two equations, the state-space model has
been broadly applied in signal processing (Smith and
Brown, 2003). A first equation describes how the hid-
den state or latent process is observed and a second
(state) equation that defines the evolution of the pro-
cess through time. Based on the formulation given by
(Haverkampet al., 1996), we proposeidentification of
a multiple-input single-output continuous-time model
from the experimentally collected input and output
data.
Considering the state-space model in the innova-
tions form
dx(t)
dt
= Ax(t) + Bu(t) (1)
y(t) = Cx(t) + Du(t) (2)
where u(t) denotes the sampled inputs, being the
foot and shank rotations in the sagittal plane dur-
ing walking, for continuous measurements at 100 Hz
sampling frequency, with transitions from low to high
speed, and progressive variations in step length and
given the measured output reference; y(t), as the en-
trained knee joint status (actuator activation period)
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