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signals (Navarro, 2005; Dhillon, 2004; Citi, 2008). The possibility of extracting
“global” information related to grasping tasks seems more likely than information
related to the detailed kinematics and dynamics of the task. In particular, the
combination of multisite intraneural peripheral interfaces and advanced processing
techniques seems to be able to increase the amount of information that can be extracted.
Regarding upper-limb control, new models will be available thanks to extensive
clinical experimentation with primates. Motor-control information will be extracted
centrally (brain) and peripherally using different levels of invasiveness interfaces.
These include invasive implantable electrodes in the brain and in the peripheral nerves
(bidirectional neuro-prostheses), and superficial electrodes picking up EEG and EMG
signals. Research on algorithms, as well as on innovative interfaces with better signal-
to-noise ratio, will provide more accurate signals for modelling.
Innovative implantable interfaces that are able to intimately collect the neural
signal process and wirelessly transmit it to external recording systems will help the
understanding of the neuroscientific models of motor control. Biocompatibility and
implants that are implanted once and will last almost for the whole of life will be
assured by using materials delivering drugs to the tissue, thus avoiding rejection of the
external device.
Global information and precise fine manipulation upper limb motions will be well
modelled; implantable chronicle interfaces will be available, as well as highly
dexterous artificial arms, hands and legs. This will result in the market exploitation of
cybernetic functional substitutes of lost or damaged limbs.
Robotic neuro-rehabilitation
As regards upper limbs, studies have used a robotic manipulator to create velocity-
dependent forces analogous to inertial Coriolis forces in order to investigate upper limb
motor control (Shadmehr, 1994). Unifying principles of movement have emerged from
computational studies of motor control: specific models emerging from a
computational approach provide a theoretical framework for movement neuroscience.
(Wolpert, 2000). Humans are learning to stabilize unstable dynamics using the skilful
and energy-efficient strategy of selective control of impedance geometry (Burdet,
2001). Successful manipulation requires the ability both to predict the motor
commands required to grasp, lift, and move objects and also to predict the sensory
events that arise as a consequence of these commands (Flanagan, 2006). As regards
lower limbs, evidence for spinal pattern generators in cats and primates, including
humans and interaction with sensory signals from limbs was found in different studies.
For all species, the sensory feedback from the moving limb is very important to achieve
effective locomotor behaviour by adapting to the environment and compensating for
unexpected postural disturbances (Hultborn, 2007). These works have been crucially
important for the development of new rehabilitation paradigms following spinal cord
injury. Future trends are:
•
development of a comprehensive biological theory of motor control in
humans;
integration of contributions from experimental trials using traditional and
ubiquitous robotic systems for validating neuroscience-based models and
models of limb motor control;
•
