Biomedical subjects

Jonathan B Dingwell

Publications and source records attributed to Jonathan B Dingwell.

8 recordsLinked to original sources

PubMed2006

Peripheral neuropathy does not alter the fractal dynamics of stride intervals of gait.

The purpose of this study was to determine the effect (if any) of significant sensory loss on the long-range correlations normally observed in the stride intervals of human gait. Fourteen patients with severe peripheral neuropathy and 12 gender-, age-, height-, and weight-matched nondiabetic controls participated. Subjects walked around an approximately 200-m open-level walkway for 10 min at their comfortable pace. Continuous knee joint kinematics were recorded and used to calculate a stride interval time series for each subject. Power spectral density and detrended fluctuation analyses were used to determine whether these stride intervals exhibited long-range correlations. If the loss of long-range correlations indicates deterioration of the central control of gait, then changes in peripheral sensation should have no effect. If instead the loss of long-range correlations is a consequence of a general inability to regulate gait cycle timing, then a similar loss should occur in patients with peripheral locomotor disorders. Both power spectral density analyses and detrended fluctuation analyses showed that temporal correlations in the stride times of neuropathic and control subjects were statistically identical (P = 0.954 and P = 0.974, respectively), despite slower gait speeds (P = 0.008) and increased stride time variability (P = 0.036) among the neuropathy patients. All subjects in both groups exhibited long-range correlations. These findings demonstrate that the normal long-range correlation structure of stride intervals is unaltered by significant peripheral sensory loss. This further supports the hypothesis that the central nervous system is involved in the regulation of long-range correlations.

Aged↗

PubMed2006

The effects of sensory loss and walking speed on the orbital dynamic stability of human walking.

Peripheral sensory feedback is believed to contribute significantly to maintaining walking stability. Patients with diabetic peripheral neuropathy have a greatly increased risk of falling. Previously, we demonstrated that slower walking speeds in neuropathic patients lead to improved local dynamic stability. However, all subjects exhibited significant local instability during walking, even though no subject fell or stumbled during testing. The present study was conducted to determine if and how significant changes in peripheral sensation and walking speed affect orbital stability during walking. Trunk and lower extremity kinematics were examined from two prior experiments that compared patients with significant neuropathy to healthy controls and walking at multiple different speeds in young healthy subjects. Maximum Floquet multipliers were computed for each time series to quantify the orbital stability of these movements. All subjects exhibited orbitally stable walking kinematics, even though these same kinematics were previously shown to be locally unstable. Differences in orbital stability between neuropathic and control subjects were small and, with the exception of knee joint movements (p=0.001), not statistically significant (0.380p0.946). Differences in knee orbital stability were not mediated by differences in walking speed. This was supported by our finding that although orbital stability improved slightly with slower walking speeds, the correlations between walking speed and orbital stability were generally weak (r(2)16.7%). Thus, neuropathic patients do not gain improved orbital stability as a result of slowing down and do not experience any loss of orbital stability because of their sensory deficits.

Algorithms↗

PubMed2006

A nonlinear approach to tracking slow-time-scale changes in movement kinematics.

Degenerative processes like repetitive strain injuries (RSIs) cause normal movement patterns to change slowly over time. Accurately tracking how these disease/injury processes evolve over time and predicting their future progression could allow early intervention and prevent further deterioration. However, these processes often cannot be measured directly and first-principles models of these processes and how they affect movement control are highly complex and difficult to derive analytically. This study was conducted to determine if algorithms developed to track damage accumulation in mechanical systems without requiring first-principles models or direct measurements of the damage itself could also track a similar "hidden" process in a biomechanical context. Five healthy adults walked on a motorized treadmill at their preferred speed, while the treadmill inclination angle was slowly increased from 0 degrees (level) to approximately +8 degrees . Sagittal plane kinematics for the left hip, knee, and ankle joints were computed. The treadmill inclination angle was independently recorded and defined the "damage" to be tracked. Scalar tracking metrics were computed from the lower extremity walking kinematics. These metrics exhibited strong cubic relationships with treadmill inclination (88.9%<or=r(2)<or=98.2%; p<0.001). These results demonstrate that the proposed approach may also be well suited to tracking and predicting slow-time-scale degenerative biological processes like muscle fatigue or RSIs. This possibility is potentially quite powerful because it suggests that easily obtainable biomechanical data can provide unique and valuable insights into the dynamics of "hidden" biological processes that cannot be easily measured themselves.

Adult↗

PubMed2006

A direct comparison of local dynamic stability during unperturbed standing and walking.

Standing and walking are very different tasks. It might be reasonable, therefore, to assume that the mechanisms used to maintain the stability of standing and walking should be quite different. However, many studies have shown that postural stability measures can generally predict risk of falls, even though most falls occur during locomotor tasks and not during postural tasks. This suggests that there is at least some commonality among the mechanisms governing the control of both standing and walking. The present study was conducted to determine whether the postural stability either is or is not directly related to locomotor stability. Twenty healthy adults, age 18-73 years, walked on a motorized treadmill at their preferred walking speed for three trials of 5 min. They also stood on a force plate for three trials of 5 min. Both tasks were performed without imposing any additional external perturbations. The motion of each subject's trunk segment was recorded and described using a multi-dimensional state space defined in the same manner for both tasks. Local dynamic stability was quantified from the mean divergence over time of locally perturbed trajectories in state space, which was parameterized as a double exponential process. Divergence parameters were compared to determine the relationship between local dynamic stability during standing and walking. Standing and walking exhibited local dynamic stability properties that were significantly different (P<0.001) and not correlated (P>0.1). Divergence parameters were also compared to traditional center of pressure (COP) measures obtained from standing trials. COP measures were significantly correlated to local divergence parameters for standing, but not to those for walking. This study provides direct evidence that the mechanisms governing standing and walking stability are significantly different.

Adolescent↗

PubMed2006

Intra-session reliability of local dynamic stability of walking.

While local dynamic stability measures have been successfully used to characterize walking stability, they require long continuous walking data, which may be difficult to obtain from a clinical population. We investigated the amount of walking data necessary to obtain reliable measures of local dynamic stability. Twenty healthy adults walked on a motorized treadmill at their self-selected speed for three trials of 5 min each. Trunk motion was used to construct a 12-dimensional state space comprised of the linear and angular positions and velocities. Mean divergence of locally perturbed trajectories was calculated as a measure of local dynamic stability using the first 1-5 min of data from each trial. Exponential divergence rates were quantified. Divergence was also parameterized using a double-exponential function. Intra-class correlation coefficients ICC(2,1) were calculated for each divergence measure for each trial length. ICC(2, 1) values increased with trial length, and reached 0.5-0.9. Good reliability was obtained for short-term measures for trial lengths of 2 and 3 min, but 5 min was not adequate to estimate the long-term coefficients based on a single trial.

Adolescent↗

PubMed2006

Kinematic variability and local dynamic stability of upper body motions when walking at different speeds.

A ubiquitous characteristic of elderly and patients with gait disabilities is that they walk slower than healthy controls. Many clinicians assume these patients walk slower to improve their stability, just as healthy people slow down when walking across ice. However, walking slower also leads to greater variability, which is often assumed to imply deteriorated stability. If this were true, then slowing down would be completely antithetical to the goal of maintaining stability. This study sought to resolve this paradox by directly quantifying the sensitivity of the locomotor system to local perturbations that are manifested as natural kinematic variability. Eleven young healthy subjects walked on a motorized treadmill at five different speeds. Three-dimensional movements of a single marker placed over the first thoracic vertebra were recorded during continuous walking. Mean stride-to-stride standard deviations and maximum finite-time Lyapunov exponents were computed for each time series to quantify the variability and local dynamic stability, respectively, of these movements. Quadratic regression analyses of the dependent measures vs. walking speed revealed highly significant U shaped trends for all three mean standard deviations, but highly significant linear trends, with significant or nearly significant quadratic terms, for five of the six finite-time Lyapunov exponents. Subjects exhibited consistently better local dynamic stability at slower speeds for these five measures. These results support the clinically based intuition that people who are at increased risk of falling walk slower to improve their stability, even at the cost of increased variability.

Adult↗

PubMed2003

Experimentally confirmed mathematical model for human control of a non-rigid object.

Determining the principles used to plan and execute movements is a fundamental question in neuroscience research. When humans reach to a target with their hand, they exhibit stereotypical movements that closely follow an optimally smooth trajectory. Even when faced with various perceptual or mechanical perturbations, subjects readily adapt their motor output to preserve this stereotypical trajectory. When humans manipulate non-rigid objects, however, they must control the movements of the object as well as the hand. Such tasks impose a fundamentally different control problem than that of moving one's arm alone. Here, we developed a mathematical model for transporting a mass-on-a-spring to a target in an optimally smooth way. We demonstrate that the well-known "minimum-jerk" model for smooth reaching movements cannot accomplish this task. Our model extends the concept of smoothness to allow for the control of non-rigid objects. Although our model makes some predictions that are similar to minimum jerk, it predicts distinctly different optimal trajectories in several specific cases. In particular, when the relative speed of the movement becomes fast enough or when the object stiffness becomes small enough, the model predicts that subjects will transition from a uni-phasic hand motion to a bi-phasic hand motion. We directly tested these predictions in human subjects. Our subjects adopted trajectories that were well-predicted by our model, including all of the predicted transitions between uni- and bi-phasic hand motions. These findings suggest that smoothness of motion is a general principle of movement planning that extends beyond the control of hand trajectories.

Algorithms↗

PubMed2002

Manipulating objects with internal degrees of freedom: evidence for model-based control.

There is substantial evidence that humans possess an accurate and adaptable internal model of the dynamics of their arm that is utilized by the nervous system for controlling arm movements. However, it is not known if such model-based strategies are used for controlling dynamical systems outside the body. The need to predict events in the external world is not restricted to the execution of reaching movements or to the handling of rigid tools. Model-based control may also be critical for performing functional tasks with non-rigid objects such as stabilizing a cup of coffee. The present study investigated the strategies used by humans to control simple mass-spring objects. Subjects made straight line reaching movements to a target while interacting with a robotic manipulandum that simulated the dynamics of a one-dimensional mass on a spring. After learning, neither hand nor object kinematics returned to those of free reaching, suggesting that this task was not learned as a perturbation of free reaching. Although there are control strategies (such as slowing the movement of the hand) that would require little or no knowledge of object dynamics, subjects did not adopt these strategies. Instead, they tailored their motor commands to the particular object being manipulated. When object parameters were unexpectedly altered in a way that required no changes in kinematics to successfully complete the task, subjects nonetheless exhibited substantial kinematic deviations. These deviations were consistent with those predicted by a model of the arm-plus-object system driven by a low-impedance controller that incorporated an explicit inverse model of arm-plus-object dynamics. The observed behavior could not be reproduced by a controller that relied on modulating hand impedance alone with no inverse model. These results were therefore consistent with the hypothesis that subjects learn to control the kinematics of manipulated objects by forming an internal model that specified the forces to be exerted by the hand on the object to induce the desired motion of that object.

Adaptation, Physiological↗