Search

Humans experience small fluctuations in their gait when walking on uneven terrain. The fluctuations deviate from the steady, energy-minimizing pattern for level walking and have no obvious organization. But humans often look ahead when they walk, and could potentially plan anticipatory fluctuations for the terrain. Such planning is only sensible if it serves some an objective purpose, such as maintaining constant speed or reducing energy expenditure, that is also attainable within finite planning capacity. Here, we show that humans do plan and perform optimal control strategies on uneven terrain. Rather than maintaining constant speed, they make purposeful, anticipatory speed adjustments that are consistent with minimizing energy expenditure. A simple optimal control model predicts economical speed fluctuations that agree well with experiments with humans (N = 12) walking on seven different terrain profiles (correlated with model p = 0.55 ± 0.11, P < 0.05 all terrains). Participants made repeatable speed fluctuations starting about six to eight steps ahead of each terrain feature (up to ±7.5 cm height difference each step, up to 16 consecutive features). Nearer features matter more, because energy is dissipated with each succeeding step's collision with ground, preventing momentum from persisting indefinitely. A finite horizon of continuous look-ahead and motor working space thus suffice to practically optimize for any length of terrain. Humans reason about walking in the near future to plan complex optimal control sequences. [ABSTRACT FROM AUTHOR]

ABSTRACT. The authors employed a virtual environment to investigate how humans use haptic and visual feedback in a simple, rhythmic object-manipulation task. The authors hypothesized that feedback would help participants [...]

Background. We quantified age-related decreases in the ability of female participants to generate whole leg movements about the hip. Methods. We measured maximum hip strength and hip velocity in 12 young and 12 older healthy women. Both capabilities could help fall prevention by contributing to fast leg movements. We also measured maximum velocities as a function of isotonic load. Results. Young participants produced 107.6 [+ or -] 25.4 N-m (mean [+ or -] SD) isometric torque in flexion and 109.3 [+ or -] 22.3 N-m in extension. Older participants produced 22% and 31% lower torques, respectively (p < .001). Young participants generated maximum velocities of 362.8 [+ or -] 51.5 [degrees]/s in flexion and 371.5 [+ or -] 54.2 [degrees]/s in extension. Older participants produced 16% lower velocities in both directions (p < .001). Older participants also produced lower velocities as a function of load (p < .001), and lower maximum power (p < .001). Conclusion. Both maximum strength and velocity contribute to reduced ability to move the leg quickly with age.

We modified an irreducibly simple model of passive dynamic walking to walk on level ground, and used it to study the energetics of walking and the preferred relationship between speed and step length in humans. Powered walking was explored using an impulse applied at toe-off immediately before heel strike, and a torque applied on the stance leg. Although both methods can supply energy through mechanical work on the center of mass, the toe-off impulse is four times less costly because it decreases the collision loss at heel strike. We also studied the use of a hip torque on the swing leg that tunes its frequency but adds no propulsive energy to gait. This spring-like actuation can further reduce the collision loss at heel strike, improving walking energetics. An idealized model yields a set of simple power laws relating the toe-off impulses and effective spring constant to the speed and step length of the corresponding gait. Simulations incorporating nonlinear equations of motion and more realistic inertial parameters show that these power laws apply to more complex models as well. [DOI: 10.1115/1.1427703]

The question posed in this study is whether optimal control and state estimation can explain selection of control strategies used by humans, in response to small perturbations to stable upright balance. To answer this question, a human sensorimotor control model, compatible with previous work by others, was assembled. This model incorporates linearized equations and full-state feedback with provision for state estimation. A form of gain-scheduling is employed to account for nonlinearities caused by control and biomechanical constraints. By decoupling the mechanics and transforming the controls into the space of experimentally observed strategies, the model is made amenable to the study of a number of possible control objectives. The objectives studied include cost functions on the state deviations, so as to control the center of mass, provide a stable platform for the head, or maintain upright stance, along with a cost function on control effort. Also studied was the effect of time delay on the stability of controls produced using various control strategies. An objective function weighting excursion of the center of mass and deviations from the upright stable position, while taking advantage of fast modes of the system, as dictated by inertial parameters and musculoskeletal geometry, produces a control that reasonably matches experimental data. Given estimates of sensor performance, the model is also suited for predicting of uncertainty in the response.