Introduction
Walking is a challenging activity for people suffering from lower-limb
spasticity, that occur for many reasons including neurological diseases
such as Cerebral Palsy (CP), spinal cord disorders or strokes (Cerebral
Accident for CA). CP is a disorder that affects muscle tone and motor
skills, in a way that doesn’t allow persons to move in a coordinated and
purposeful way. Spinal cord disorders are due to injuries, infections,
blocked blood supply and compression by a tumor or a fracture. For CA,
the poor blood flow to the brain results in cells death, which leads to
death or, in case of survival, to muscle paralysis. This induce a gait
disorder and other disabilities.
The use of a rehabilitation exoskeleton helps to improve the social status of paraplegics. Since the beginning of the second half of the 20th century,
exoskeletons witnessed huge progress in the field of gait
rehabilitation. this progress is still ongoing in the 21st century while
prototypes are being developed and the use of exoskeletons spans
worldwide for the paraplegics rehabilitation.
However, exoskeletons are facing real challenges in this context,
especially with regard to control: each patient has their own set of
specific movements, their own space and time parameters, kinetic
parameters, their own electromyography signals etc…
Therefore, it is important to find a solution to better control
exoskeletons in order to rehabilitate patients and compensate walking
disorders to ensure a better recovery.
The walking disorder caused by the spasticity of the muscles, appears by
kinematic parameters where the trajectory of the joints during the walk
becomes disordered.
From this point on, understanding normal and abnormal gait is a
prerequisite in order to be able to diagnose and analyze any gait
disorder or pathology.
This understanding stems from the extraction of biomedical signals (EMG ) which
will be combined to joint kinematics.
In other words, the idea is to find the relation between the muscle
force which cause the joint movement (knee and hip) and the joint angles
during a walking cycle to find a steering function for or the control of
rehabilitation exoskeleton.
Paving the way for a better movement compensation for patients.in our
work we have tried to find a relation between muscular co-contraction
index and gait angle, paving the way for a find a signal for
rehabilitation exoskeleton control for paraplegics movement compensation.
LISV team (system engineering laboratory of
Versailles : versailles University ), develops the design of a rehabilitation exoskeleton, this
exoskeleton has four degrees of freedom, where the actuators are
connected to two active assets the knee and hip and the other two
degrees of freedom are passive like springs on the ankles, its advantage
is that it is deformable in a way that it can be used by paraplegics of
different ages from 10 years.
We chose to test the cases of Healthy people (HP) (to determine the baseline) and paraplegics : CP and CA (for rehabilitation cases) by databases
already existing at Endicap laboratory of Garches Hospital (Raymond Poincare)
Our thesis goal, is to find a control method to use LISV exoskeleton to rehabilitation and to
assist paraplegics by regulating their strength required for a successful gait
movement. This assistance must be introduced to correct their walking
movement without sacrificing the patient control priority, during the gait
cycle.
The proposed method for exoskeleton control (neuro-motor) is to find
inputs related directly to the following patient’s movement parameters
to control Exoskeleton:
-
Walking kinematic study of patient for finding the angles
“Ɵ” of (Flexion/extension) for each lower limb
joints: Hip and Knee, during the Gait cycle.
-
Walking Electromyography study (EMG) of patient and his processing for
finding the co-contraction index “CCI “between lower
limb muscles pairs(Agonist/Antagonist) , during the Gait cycle.
-
Finding the relation between the difference of angle
“∆Ɵ” (normal/abnormal gait) for each joint and the
co-contraction index “ICC” (abnormal gait).
priori knowledge:
Related Works
the antagonistic muscles can contract at the same time as the agonists
in a large number of circumstances. This is called co-contraction, which
characterizes the simultaneous activation of the agonist and antagonist
muscles within the same joint and acting on the same plane
Olney, 1985
Muscle co-contraction is an important variable that can be used to
evaluate the function of the walking muscle causing movement of a joint
such as the knee and hip.
When it comes to bi-articular muscles such as the quadriceps and
hamstrings that affect the same at the hip and knee, for example,
In normal human movement, the presence of the agonist-antagonist
co-contraction and its degree remain incompletely elucidated (Smith,
1981, Damiano, 1993).
The level of antagonistic co-contraction seems to increase
proportionally with the speed of the movement (Wachholder and
Altenburger 1925, Barnett and Harding, 1955), to vary with the degree of
inertia during the movement (Lestienne and Bouisset, 1968), according to
the muscular group considered. (Patton and Mortensen, 1971) and
according to the type of agonist contraction, being superior in
concentric rather than eccentric mode (Kellis and Unnhitan, 1999).
In isometric conditions, the antagonistic co-contraction seems to
increase according to the agonist activation, being proportionally
higher during the maximal voluntary contraction (Yang and Winter, 1983,
Hébert et al., 1991); it also appears to be dependent on the position of
other joint segments (eg forearm position in pronation or supination on
the upper limb), and angular variations (Funk et al., 1987).
The level of antagonistic co-contraction may also be partially dependent
on sex and age (Kelly and Unnithan 1999, Osternig et al., 1995, Baratta
et al., 1988, Morse et al. for others (Seger and Thorstensson, 1994,
Frost et al., 1997, Unnithan et al., 1996b, Spiegel et al., 1996,
Peterson and Martin, 2010).
In the functional operating condition, antagonistic co- contraction is a
common feature of several muscles. For example, the hamstrings and the
femoral right are described as simultaneously active in order to provide
support for the trunk when walking (Carlsöö and Nordstrand, 1968).
Antagonistic co-contractions seems to be a rule as soon as a precision
requirement arises (Smith, 1981, Humprey, 1982, Aagaard et al., 2000)
constituting a central element of the means by which the nervous system
adjusts movements at all times. even after learning dynamics (Darainy
and Ostry, 2008) reflecting the adaptive character of the neuromuscular
system according to the desired movement (Bouisset and Lestienne, 1974).
A more elaborate view was proposed by Basmajian in 1977, which suggested
that motor skill acquisition is expressed by selective inhibition of
unnecessary muscle activities rather than activation of additional motor
units. Antagonist co-contraction is thus rather an indicator of the
degree of learning, in the same way as in the child under development.
During children motor development , the movement is characterized by a
sequence of massive antagonistic co-contractions in the first months of
life, and a reciprocal inhibition gradually more and more widespread,
which becomes fully functional in a nervous system. mature (Thelen et
al., 1983, Gatev, 1972).
As postulate disciples, some authors report that the degree of
antagonistic co-contraction may decrease after the learning phenomenon,
although the effects are not lasting over time (Carolane and Cafarelli,
1992, Gribble et al., 2003).
Thus, the antagonistic co-contraction could reflect a defect of strategy
of the nervous system when there is an uncertainty of the task to be
performed as in the case of the elderly who, having often difficulties
in controlling the level of force reduction, adopt co-activation of
antagonists (Spiegel et al., 1996, Seidler-Dobrin et al., 1998), or
cases of central fatigue occurrence (Rodrigues et al., 2009) .
Of the existing co-contraction index that are commonly used to quantify
the co-contraction of muscle activities, the ratio of antagonistic
activity to total muscle activities
EMG Normalization:
The many factors involved in the EMG signal represent obstacles to the
direct comparison of EMG amplitudes in microvolts between different
subjects as well as for the same subject tested at different sessions.
One solution is then to standardize the electrical units, ie to express
the intensity of the EMG with respect to a reference value. Different
methods exist for calculating this value reference, especially in the
study of walking, and the criterion of ideal normalization does not yet
enjoy a unanimous consensus (Yang and Winter, 1984). A reference value
used in the majority of studies is that obtained during maximal
isometric contraction (Perry 1992, De Luca 1997). Apart from the
interpolated stimulation method (Denny-Brown and Sherrington, 1928), the
aim is to test the subject during maximal isometric effort by measuring
the EMG of the muscle in parallel. The average of the IEMG or the RMS is
then read as reference values. Relative values are then derived,
expressed as a percentage of the reference value. Admittedly, this
measure does not make it possible to obtain the actual maximum voluntary
force of which a subject is capable, representing a potential source of
error (Allen et al., 1995). However, it has a high intra-individual
reproducibility index (Allen et al., 1995), and a better intra and
inter-individual reproducibility score than dynamic EMG values
\cite{Knutson_1994}.
This normalization method can be used for the quantification of agonist
recruitment but also for the quantification of antagonistic recruitment
in the estimation of co-contraction degree (Gracies et al., 2009, Vinti
et al., 2012ab, Table 1, index 3). In the literature, for the
quantification of antagonistic co-contraction under static or dynamic,
physiological conditions \cite{Knutson1994,Kellis2003} (Olney, 1985, Solomonow et al., 1986, 1987,
1988, Knutson et al., 1994, Falconer and Winter, 1995) and pathological (Unnithan et al., 1996ab, Frost et al., 1997,
Levin et al., 1994, 2000, Ikeda et al., 1998, Gracies et al., 2009,
Vinti et al., 2012ab ), various methods have been used ranging from
simple electromyographic measurements (Knutson et al., 1994; Unnhitan et
al., 1996ab) to advanced mathematical models (Olney, 1985, Solomonow et
al., 1986).