High-level motor planning allows flexible walking at different gait patterns in a neuromechanical model.

Humans can freely adopt gait parameters like walking speed, step length, or cadence on the fly when walking. Planned movement that can be updated online to account for changes in the environment rather than having to rely on habitual, reflexive control that is adapted over long timescales. Here we present a neuromechanical model that accounts for this flexibility by combining movement goals and motor plans on a kinematic task level with low-level spinal feedback loops. We show that the model can walk at a wide range of different gait patterns by choosing a small number of high-level control parameters representing a movement goal. A larger number of parameters governing the low-level reflex loops in the spinal cord, on the other hand, remain fixed. We also show that the model can generalize the learned behavior by recombining the high-level control parameters and walk with gait patterns that it had not encountered before. Furthermore, the model can transition between different gaits without the loss of balance by switching to a new set of control parameters in real time.

Estimating the time structure of descending activation that generates movements at different speeds.

In targeted movements of the hand, descending activation patterns must not only generate muscle activation but also adjust spinal reflexes from stabilizing the initial to stabilizing the final postural state. We estimate descending activation patterns that change minimally while generating a targeted movement within a given movement time based on a model of the biomechanics, the muscle dynamics, and the stretch reflex. The estimated descending activation patterns predict human movement trajectories quite well. Their temporal structure varies across workspace and with movement speed, from monotonic profiles for slow movements to nonmonotonic profiles for fast movements. Descending activation patterns at different speeds thus do not result from a mere rescaling of invariant templates but reflect varying needs to compensate for interaction torques and muscle dynamics. The virtual attractor trajectories, on which active muscle torques are zero, lie within reachable workspace and are largely invariant when represented in end-effector coordinates. Their temporal structure along movement direction changes from linear ramps to "N-shaped" profiles with increasing movement speed.NEW & NOTEWORTHY The descending activation patterns driving movement must be integrated with spinal reflexes, which would resist movement if left unchanged. We estimate the descending activation patterns at different movement speeds using a model of the stretch reflex and of muscle and limb dynamics. The descending activation patterns we find are temporally structured to compensate for interaction torques as predicted by internal models but also shift the reflex threshold, solving the posture-movement problem.

A neuromuscular model of human locomotion combines spinal reflex circuits with voluntary movements.

Existing models of human walking use low-level reflexes or neural oscillators to generate movement. While appropriate to generate the stable, rhythmic movement patterns of steady-state walking, these models lack the ability to change their movement patterns or spontaneously generate new movements in the specific, goal-directed way characteristic of voluntary movements. Here we present a neuromuscular model of human locomotion that bridges this gap and combines the ability to execute goal directed movements with the generation of stable, rhythmic movement patterns that are required for robust locomotion. The model represents goals for voluntary movements of the swing leg on the task level of swing leg joint kinematics. Smooth movements plans towards the goal configuration are generated on the task level and transformed into descending motor commands that execute the planned movements, using internal models. The movement goals and plans are updated in real time based on sensory feedback and task constraints. On the spinal level, the descending commands during the swing phase are integrated with a generic stretch reflex for each muscle. Stance leg control solely relies on dedicated spinal reflex pathways. Spinal reflexes stimulate Hill-type muscles that actuate a biomechanical model with eight internal joints and six free-body degrees of freedom. The model is able to generate voluntary, goal-directed reaching movements with the swing leg and combine multiple movements in a rhythmic sequence. During walking, the swing leg is moved in a goal-directed manner to a target that is updated in real-time based on sensory feedback to maintain upright balance, while the stance leg is stabilized by low-level reflexes and a behavioral organization switching between swing and stance control for each leg. With this combination of reflex-based stance leg and voluntary, goal-directed control of the swing leg, the model controller generates rhythmic, stable walking patterns in which the swing leg movement can be flexibly updated in real-time to step over or around obstacles.

Interactions Between Different Age-Related Factors Affecting Balance Control in Walking.

Maintaining balance during walking is a continuous sensorimotor control problem. Throughout the movement, the central nervous system has to collect sensory data about the current state of the body in space, use this information to detect possible threats to balance and adapt the movement pattern to ensure stability. Failure of this sensorimotor loop can lead to dire consequences in the form of falls, injury and death. Such failures tend to become more prevalent as people get older. While research has established a number of factors associated with higher risk of falls, we know relatively little about age-related changes of the underlying sensorimotor control loop and how such changes are related to empirically established risk factors. This paper approaches the problem of age-related fall risk from a neural control perspective. We begin by summarizing recent empirical findings about the neural control laws mapping sensory input to motor output for balance control during walking. These findings were established in young, neurotypical study populations and establish a baseline of sensorimotor control of balance. We then review correlates for deteriorating balance control in older adults, of muscle weakness, slow walking, cognitive decline, and increased visual dependency. While empirical associations between these factors and fall risk have been established reasonably well, we know relatively little about the underlying causal relationships. Establishing such causal relationships is hard, because the different factors all co-vary with age and are difficult to isolate empirically. One option to analyze the role of an individual factor for balance control is to use computational models of walking comprising all levels of the sensorimotor control loop. We introduce one such model that generates walking movement patterns from a short list of spinal reflex modules with limited supraspinal modulation for balance. We show how this model can be used to simulate empirical studies, and how comparison between the model and empirical results can indicate gaps in our current understanding of balance control. We also show how different aspects of aging can be added to this model to study their effect on balance control in isolation.