Locomotion prediction of bipedal walking in monkeys
As a consequence of the development of locomotion rehabilitation and the great interest
of extracting information directly from brain activity, a lot of work have be done regard-
ing the prediction of locomotion from brain records (see pionner wark of N.Fitzsimmons).
Currently, accurate real-time prediction of joints in a Cartesian coordinate system
are possible. Such predictions are expected to have possible applications, such as assist-
ing old people walking or, in a larger horizon, opening the way to the development of
efficient exoskeleton. Movement prediction is crucial for the development of tools that
could assists disabled, since it will allow anticipation of the movements and thus permit
more fluidity in the movements.
In this project, we started developing a new way of representing the movement dur-
ing locomotion, based on joint angle instead of expressing joints in a Cartesian coordi-
nate system. The method uses a specific oscillator, developed by J. Kieboom, capable
of generating an arbitrary waveform to dynamically reconstruct the joint angles. On the
one hand, this will permit to reduce amount of information needed to accurately predict
the joint position and on the other hand, by the intrinsic use of differential equations,
this will offers stability regarding perturbations, and could easily be extended to take
into account crucial information, such as feedback. Howeve, the method described has
certain drawbacks that will be extensively discussed in chapter 4.
The project is separated in two parts. In the first part, we will present the method and
apply it to predict the locomotion of a monkey walking on a treadmill. The parameters
used for the reconstruction will be directly extracted from the walking, and not from
brain records. In the second part, we will analyze the relationships between the different
extracted parameters, and assess their relative importance in locomotion modulation.