Planar covariation of elevation angles in prosthetic gait.

In order to achieve efficacious walking, transfemoral amputees must adapt coordination within both the artificial and the sound lower limb. We analyzed kinematic strategies in amputees using the planar covariation of lower limb segments approach. When the elevation angles of the thigh, shank and foot are plotted one versus the others, they describe a regular loop which lies close to a plane in normal adults' gait. Orientation of this plane changes with increased speed, in relation to mechanical energetic saving. We used an opto-electronic device to record the elevation angles of both limbs' segments of novice and expert transfemoral amputees and compared them to those of control subjects. The statistical structure underlying the distribution of these angles was described by principal component analysis and Fourier transform. The typical elliptic loop was preserved in prosthetic walking, in both limbs in both novice and expert transfemoral amputees. This reflects a specific control over the thigh elevation angle taking into account knowledge of the other elevation angles throughout the gait cycle. The best-fitting plane of faster trials rotates around the long axis of the gait loop with respect to the plane of slower trials for control subjects, and even more for the sound limb of expert amputees. In contrast, plane rotation is very weak or absent for the prosthetic limb. We suggest that these results reveal a centrally commanded compensation strategy.

Rhythmic muscular activation pattern for fast figure-eight movement.

OBJECTIVE: To address the question of how the CNS generates muscle activation patterns for complex gestures, we have chosen to study a figure-eight movement. We hypothesized that the well defined rhythmic aspect of this figure will provide further insights into the temporal features of multi-muscular commands. METHODS: Subjects performed, as fast as possible, figure-eights initiated in the center of the figure with 4 different initial directions and 2 positions of the shoulder. We extracted the temporal modulation of the EMG patterns by calculating conjugate cross-correlation functions. RESULTS: (1) The muscular command was tuned with respect to the rotational direction of the figure-eight, (2) two sets of synergistic muscles acted in a reciprocal mode, and (3) these reciprocal commands presented an invariant temporal correlation with the spatial component of the velocity having the highest frequency. CONCLUSION: Our results suggest that the rhythmic features of certain drawing movements favor the partitioning of the muscles into synergistic groups acting in a reciprocal mode. The inclusion of an individual muscle in one group or the other takes into account the expected number of changes of direction in the movement as a whole. SIGNIFICANCE: Muscular temporal synergies may depend on the rhythmic features of the trajectory.

A dynamic recurrent neural network for multiple muscles electromyographic mapping to elevation angles of the lower limb in human locomotion.

This paper describes the use of a dynamic recurrent neural network (DRNN) for simulating lower limb coordination in human locomotion. The method is based on mapping between the electromyographic signals (EMG) from six muscles and the elevation angles of the three main lower limb segments (thigh, shank and foot). The DRNN is a fully connected network of 35 hidden units taking into account the temporal relationships history between EMG and lower limb kinematics. Each EMG signal is sent to all 35 units, which converge to three outputs. Each output neurone provides the kinematics of one lower limb segment. The training is supervised, involving learning rule adaptations of synaptic weights and time constant of each unit. Kinematics of the locomotor movements were recorded and analysed using the opto-electronic ELITE system. Comparative analysis of the learning performance with different types of output (position, velocity and acceleration) showed that for common gait mapping velocity data should be used as output, as it is the best compromise between asymptotic error curve, rapid convergence and avoidance of bifurcation. Reproducibility of the identification process and biological plausibility were high, indicating that the DRNN may be used for understanding functional relationships between multiple EMG and locomotion. The DRNN might also be of benefit for prosthetic control.