Biomedical subjects

S D Prentice

Publications and source records attributed to S D Prentice.

7 recordsLinked to original sources

PubMed2001

Artificial neural network model for the generation of muscle activation patterns for human locomotion.

Skilled locomotor behaviour requires information from various levels within the central nervous system (CNS). Mathematical models have permitted researchers to simulate various mechanisms in order to understand the organization of the locomotor control system. While it is difficult to adequately characterize the numerous inputs to the locomotor control system, an alternative strategy may be to use a kinematic movement plan to represent the complex inputs to the locomotor control system based on the possibility that the CNS may plan movements at a kinematic level. We propose the use of artificial neural network (ANN) models to represent the transformation of a kinematic plan into the necessary motor patterns. Essentially, kinematic representation of the actual limb movement was used as the input to an ANN model which generated the EMG activity of 8 muscles of the lower limb and trunk. Data from a wide variety of gait conditions was necessary to develop a robust model that could accommodate various environmental conditions encountered during everyday activity. A total of 120 walking strides representing normal walking and ten conditions where the normal gait was modified in terms of cadence, stride length, stance width or required foot clearance. The final network was assessed on its ability to predict the EMG activity on individual walking trials as well as its ability to represent the general activation pattern of a particular gait condition. The predicted EMG patterns closely matched those recorded experimentally, exhibiting the appropriate magnitude and temporal phasing required for each modification. Only 2 of the 96 muscle/gait conditions had RMS errors above 0.10, only 5 muscle/gait conditions exhibited correlations below 0.80 (most were above 0.90) and only 25 muscle/gait conditions deviated outside the normal range of muscle activity for more than 25% of the gait cycle. These results indicate the ability of single network ANNs to represent the transformation between a kinematic movement plan and the necessary muscle activations for normal steady state locomotion but they were also able to generate muscle activation patterns for conditions requiring changes in walking speed, foot placement and foot clearance. The abilities of this type of network have implications towards both the fundamental understanding of the control of locomotion and practical realizations of artificial control systems for use in rehabilitation medicine.

Adult↗

PubMed2001

Contributions of the reticulospinal system to the postural adjustments occurring during voluntary gait modifications.

To test the hypothesis that reticulospinal neurons (RSNs) are involved in the formation of the dynamic postural adjustments that accompany visually triggered, voluntary modifications of limb trajectory during locomotion, we recorded the activity of 400 cells (183 RSNs; 217 unidentified reticular cells) in the pontomedullary reticular formation (PMRF) during a locomotor task in which intact cats were required to step over an obstacle attached to a moving treadmill belt. Approximately one half of the RSNs (97/183, 53%) showed significant changes in cell activity as the cat stepped over the obstacle; most of these cells exhibited either single (26/97, 26.8%) or multiple (63/97, 65.0%) increases of activity. There was a range of discharge patterns that varied in the number, timing, and sequencing of the bursts of modified activity, although individual bursts in different cells tended to occur at similar phases of the gait cycle. Most modified cells, regardless of the number of bursts of increased discharge, or of the discharge activity of the cell during unobstructed, control, locomotion, discharged during the passage of the lead forelimb over the obstacle. Thus, 86.9% of the modified cells increased their discharge when the forelimb ipsilateral to the recording site was the first to pass over the obstacle, and 72.2% when the contralateral limb was the first. Approximately one quarter of the RSNs increased their discharge during the passage of each of the four limbs over the obstacle in both the lead (27.1%) and trail (27.9%) conditions. In general, in any one cell, the number and relative sequencing of the subsequent bursts (with respect to the lead forelimb) was maintained during both lead and trail conditions. Patterns of activity observed in unidentified cells were very similar to the RSN activity despite the diverse population of cells this unidentified group may represent. We suggest that the increased discharge that we observed in these reticular neurons reflects the integration of afferent activity from several sources, including the motor cortex, and that this increased discharge signals the timing and the relative magnitude of the postural patterns that accompany the voluntary gait modification. However, based on the characteristics of the patterns of neuronal activity in these cells, we further suggest that while individual RSNs probably contribute to the selection of different patterns of postural activity, the ultimate expression of the postural response may be determined by the excitability of the locomotor circuits within the spinal cord.

Animals↗

PubMed1999

What guides the selection of alternate foot placement during locomotion in humans.

Our goal was to understand the bases for selection of alternate foot placement during locomotion when the normal landing area is undesirable. In this study, a light spot of different shapes and sizes simulated an undesirable landing area. Participants were required to avoid stepping on this spot under different time constraints. Alternate chosen foot placements were categorised into one of eight choices. Results showed that selection of alternate foot placement is systematic. There is a single dominant choice for each combination of light spot and normal landing spot. The dominant choice minimises the displacement of the foot from its normal landing spot (less than half a foot length). If several response choices satisfy this criterion, three selection strategies are used to guide foot placement: placing the foot in the plane of progression, choosing to take a longer step over a shorter step and selecting a medial rather than lateral foot placement. All these alternate foot-placement choices require minimal changes to the ongoing locomotor muscle activity, pose minimal threat to dynamic stability, allow for quick initiation of change in ongoing movement and ensure that the locomotor task runs without interruption. Thus, alternate foot-placement choices are dependent not only on visual input about the location, size and shape of the undesirable surface, but also on the relationship between the characteristics of the undesirable surface and the normal landing area.

Adult↗

PubMed1998

Simple artificial neural network models can generate basic muscle activity patterns for human locomotion at different speeds.

A neural network model has been developed to represent the shaping function of a central pattern generator (CPG) for human locomotion. The model was based on cadence and electromyographic data obtained from a single human subject who walked on a treadmill. The only input to the model was the fundamental timing of the gait cycle (stride rate) in the form of sine and cosine waveforms whose period was equal to the stride duration. These simple signals were then shaped into the respective muscle activation patterns of eight muscles of the lower limb and trunk. A network with a relatively small number of hidden units trained with back-propagation was able to produce an excellent representation of both the amplitude and timing characteristics of the EMGs over a range of walking speeds. The results are further discussed with respect to the dependence of some muscles upon sensory feedback and other inputs not explicitly presented to the model.

Algorithms↗

PubMed1995

The role of active forces and intersegmental dynamics in the control of limb trajectory over obstacles during locomotion in humans.

The focus of this paper is to examine the contributions of active and passive forces in the control of limb trajectory over obstacles during locomotion. Kintetic analyses of the swing phase of locomotion were carried out to determine the power profiles at various joints and to parcel the joint moments into moments due to muscle action, gravitational force and motion-dependent terms. The analyses revealed that toe elevation over the obstacles was achieved primarily by flexing at the hip, knee and ankle joint. Power analyses showed that translational energy applied at the hip joint and rotational energy applied at the knee joint were modulated as functions of obstacle height. This demonstrates that increased hip and ankle joint flexion are achieved not through active muscle action but rather through passive forces induced by translational action at the hip (representing contribution by the stance limb muscles) and rotational action at the knee joint. Parcelling the joint moment terms into various components clearly shows how the nervous system exploits intersegmental dynamics to simplify control of limb elevation over obstacles and minimize energy costs.

Adult↗

PubMed1995

Modelling the time-keeping function of the central pattern generator for locomotion using artificial sequential neural network.

The paper investigates the ability of a sequential neural network to model the time-keeping function (fundamental frequency oscillation) of a central pattern generator for locomotion. The intention is not to strive for biological fidelity, but rather to ensure that the network obeys the organisational and operational principles of central pattern generators developed through empirical research. The timing function serves to produce the underlying locomotor rhythm which can be transformed by nonlinear static shaping functions to construct the necessary locomotor activation patterns. Using two levels of tonic activations in the form of a step increase, a network consisting of nine processing units was successfully trained to output both sine and cosine waveforms, whose frequencies were modified in response to the level of input activation. The network's ability to generalise was demonstrated by appropriately scaling the frequency of oscillation in response to a range of input amplitudes, both within and outside the values on which it was trained. A notable and fortunate result was the model's failure to oscillate in the absence of input activation, which is a necessary property of the CPG model. It was further demonstrated that the oscillation frequency of the output waveforms exhibited both a high temporal stability and a very low sensitivity to input noise. The results indicate that the sequential neural network is a suitable candidate to model the time-keeping functions of the central pattern generator for locomotion.

Humans↗

PubMed1991

Visual control of locomotion: strategies for changing direction and for going over obstacles.

Dynamics of gait adjustments required to go over obstacles and to alter direction of locomotion when cued visually were assessed through the measurement of ground reaction forces, muscle activity, and kinematics. The time of appearance of obstacles of varying heights, their position within the step cycle, and cue lights for direction change were varied. Direction change must be planned in the previous step to reduce the acceleration of the body center of mass toward the landing foot to 0. The inability of steering within the step cycle is due to the incapacity of muscles to rotate the body and translate it along the mediolateral axes. For obstacle avoidance, Ss systematically manipulated the gait patterns as a function of obstacle height and position and the time available within the ongoing step. Greater supraspinal involvement in control of locomotion is found.

Adult↗