The dynamic characteristics of the flexor and extensor muscles induced by neural inputs were represented by three dynamic processes: neural excitation, muscle activation and muscle contraction dynamics.
Increased muscle preparatory activity and muscle activation have also been found, leading to plyometrics possibly even preventing injury [20].
It can be used for both forward-dynamic (inputs: muscle activation and external load; outputs: motions) and inverse-dynamic (inputs: motions and external load; outputs: muscle activation) simulations.
The active torque produced by the FES-induced muscle contraction is described as two parts, namely, activation dynamics and contraction dynamics, while the passive torque of the muscle is described as passive elastic and passive viscous properties [22].
If the functional role of rotational dynamics is to serve as an oscillatory basis set for generating muscle activation patterns (e.g. electromyogram activity) (Churchland et al., 2012; Sussillo et al., 2015), then one might expect these dynamics to be absent during imagined movements, when the subject is specifically trying to avoid generating motor output.
The inverse dynamics approach does not consider muscle activation force, which adds to the compression force in the knee.
33 35 The sensitivity analysis suggested that the influence of neck muscle activation, for example, bracing for impact, on head dynamics was less than that of boundary conditions such as initial position and velocities.
The active torque of FES-induced muscle contraction is the product of contraction dynamics and activation dynamics given as τ fes = a act f fv f fl F max ma (10).
The muscle dynamics represent the product of a continuous-time nonlinear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments.
Once the learning system has been pre-trained thoroughly with data from all relevant regions and within the joint limits and muscle activation range of the arm, a stochastic OFC with learned dynamics (OFC-LD) problem can be formulated that "guides" the optimal solution towards a maximum prediction certainty, while still minimizing the energy consumption and end point reaching error.
