Walking around in dynamically changing environments require the integration of three of our sensory systems: visual, vestibular, and kinesethic. Vision is the only modality of these three sensory systems that provides information at a distance for proactively controlling locomotion (Gibson, 1958). The visual system provides information about self-motion, about body position and body segments relative to one another and the environment, and environmental information at a distance (Patla, 1998). Gibson (1979) developed the idea that everyday behaviour is controlled by perception-action coupling between an action and some specific information picked up from the optic flow that is generated by that action. Such that visual perception guides the action required to navigate safely through an environment and the action in turn alters perception. The objective of my thesis was to determine how well perception and action are coupled when approaching and walking through moving doors with dynamically changing apertures. My first two studies were grouped together and here I found that as the level of threat increased, the parameters of control changed and not the controlling mechanism. The two dominant action control parameters observed were a change in approach velocity and a change in posture (i.e. shoulder rotation). These findings add to previous work done in this area using a similar set-up in virtual reality, where after much practice participants increased success rate by decreasing velocity prior to crossing the doors. In my third study I found that visual fixation patterns and action parameters were similar when the location of the aperture was predictable and when it was not. Previous work from other researchers has shown that vision and a subsequent action are tightly coupled with a latency of about 1second. I have found that vision only tightly couples action when a specific action is required and the threat of a collision increases. My findings also point in the same direction as previous work that has shown that individuals look where they are going. My last study was designed to determine if we go where we are looking. Here I found that action does follow vision but is only loosely correlated. The most important and common finding from all the studies is that at 2 seconds prior to crossing the moving doors (any type of movement) vision seems to have the most profound effect on action. At this time variability in action is significantly lower than at prior times. I believe that my findings will help to understand how individuals use vision to modify actions in order to avoid colliding with other people or other moving objects within the environment. And this knowledge will help elderly individuals to be better able to cope with walking in cluttered environments and avoid contacting other objects.

Walking around in dynamically changing environments require the integration of three of our sensory systems: visual, vestibular, and kinesethic. Vision is the only modality of these three sensory systems that provides information at a distance for proactively controlling locomotion (Gibson, 1958). The visual system provides information about self-motion, about body position and body segments relative to one another and the environment, and environmental information at a distance (Patla, 1998). Gibson (1979) developed the idea that everyday behaviour is controlled by perception-action coupling between an action and some specific information picked up from the optic flow that is generated by that action. Such that visual perception guides the action required to navigate safely through an environment and the action in turn alters perception. The objective of my thesis was to determine how well perception and action are coupled when approaching and walking through moving doors with dynamically changing apertures. My first two studies were grouped together and here I found that as the level of threat increased, the parameters of control changed and not the controlling mechanism. The two dominant action control parameters observed were a change in approach velocity and a change in posture (i.e. shoulder rotation). These findings add to previous work done in this area using a similar set-up in virtual reality, where after much practice participants increased success rate by decreasing velocity prior to crossing the doors. In my third study I found that visual fixation patterns and action parameters were similar when the location of the aperture was predictable and when it was not. Previous work from other researchers has shown that vision and a subsequent action are tightly coupled with a latency of about 1second. I have found that vision only tightly couples action when a specific action is required and the threat of a collision increases. My findings also point in the same direction as previous work that has shown that individuals look where they are going. My last study was designed to determine if we go where we are looking. Here I found that action does follow vision but is only loosely correlated. The most important and common finding from all the studies is that at 2 seconds prior to crossing the moving doors (any type of movement) vision seems to have the most profound effect on action. At this time variability in action is significantly lower than at prior times. I believe that my findings will help to understand how individuals use vision to modify actions in order to avoid colliding with other people or other moving objects within the environment. And this knowledge will help elderly individuals to be better able to cope with walking in cluttered environments and avoid contacting other objects.

Recently it has been suggested that the use of neuromechanical simulations could be used to further our understanding of the neural control mechanisms involved in the control of animal locomotion. The models used to carry out these neuromechanical simulations typically consist of a representation of the neural control systems involved in walking and a representation of the mechanical locomotor apparatus. These separate models are then integrated to produce motion of the locomotor apparatus based on signals that are generated by the neural control models. Typically in past neuromechanical simulations of human walking the parameters of the neural control model have been specifically chosen to produce a walking pattern that resembles the normal human walking pattern as closely as possible. Relatively few of these studies have systematically tested the effect of manipulating the control parameters on the walking pattern that is produced by the locomotor apparatus. The goal of this thesis was to develop models of the locomotor control system and the human locomotor apparatus and systematically manipulate several parameters of the neural control system and determine what effects these parameters would have on the walking pattern of the mechanical model. Specifically neural control models were created of the Central Pattern Generator (CPG), feedback mechanisms from muscle spindles and contact sensors that detect when the foot was contact with the ground. Two models of the human locomotor apparatus were used to evaluate the outputs of the neural control systems; the first was a rod pendulum, which represented a swinging lower-limb, while the second was a 5-segment biped model, which included contact dynamics with the ground and a support system model to maintain balance. The first study of this thesis tested the ability of a CPG model to control the frequency and amplitude of the pendulum model of the lower-limb, with a strictly feedforward control mechanism. It was found that the frequency of the pendulum’s motion was directly linked (or entrained) to the frequency of the CPG’s output. It was also found that the amplitude of the pendulum’s motion was affected by the frequency of the CPG’s output, with the greatest amplitude of motion occurring when the frequency of the CPG matched the pendulum’s natural frequency. The effects of altering several other parameters of the pendulum model, such as the initial angle, the magnitude of the applied viscous damping or the moment arms of the muscles, were also analyzed. The second study again used the pendulum model, and added feedback to the neural control model, via output from simulated muscle spindles. The output from these spindle models was used to trigger a simulated stretch reflex. It was found that the addition of feedback led to sensory entrainment of the CPG output to the natural frequency of the pendulum. The effects of altering the muscle spindle’s sensitivity to length and velocity changes were also examined. The ability of this type of feedback system to respond to mechanical perturbations was also analyzed. The third and fourth studies used a biped model of the musculoskeletal system to assess the effects of altering the parameters of the neural control systems that were developed in the first two studies. In the third study, the neural control system consisted only of feedforward control from the CPG model. It was found that the walking speed of the biped model could be controlled by altering the frequency of the CPG’s output. It was also observed that variability of the walking pattern was decreased when there was a moderate level of inhibition between the CPGs of the left and right hip joints. The final study added feedback from muscle receptors and from contact sensors with the ground. It was found that the most important source of feedback was from the contact sensors to the extensor centres of the CPG. This feedback increased the level of extensor activity and produced significantly faster walking speeds when compared to other types of feedback. This thesis was successful in testing the effects of several control parameters of the neural control system on the movement of mechanical systems. Particularly important findings included the importance of connectivity between the CPGs of the left and right hip joints and positive feedback regarding the loading of the limb for establishing an appropriate forward walking speed. It is hoped that the models developed in this thesis can form the basis of future neuromechanical models and that the simulations carried out in this thesis help provide a better understanding of the interactions between neural and mechanical systems during the control of locomotion.

<p>Reconstructing locomotor patterns from fossils is crucial for understanding the origins of primates and important transitions in various primate clades. Recent studies suggest that the semicircular canals of the inner ear provide evidence about locomotion. The canals sense rotational head accelerations and drive reflexes essential for normal movement. Because bony aspects of canal morphology influence canal sensitivity, this system can be studied in osteologic specimens and fossils. Variation in canal morphology in living and, by inference, extinct primates has been attributed to interspecific differences in locomotor behavior. However, the manner in which movement selects for canal morphology is debated, alternative scenarios are plausible, and no relevant measurements are available documenting head movements in primates.</p><p>To refine proposed links between canal morphology and locomotor function, and to resolve conflicting functional interpretations, this study examines head rotations in lemurs and lorises exhibiting diverse locomotor behaviors. Three-dimensional kinematic analyses were used to characterize angular velocities of the head during locomotion. These data are used to test hypotheses concerning intraspecific, interspecific, and body-size dependent variation in head rotations. Cranial CT scans are used to model canal sensitivity to rotations in different directions. Observed patterns of head rotation are compared to predicted patterns of sensitivity to test hypotheses about the relationship between locomotor behavior and canal design.</p><p>Evaluation of existing locomotor inferences reveals that brain size exerts a significant effect on canal size and that the prevailing equations for predicting agility from body and canal size are highly inaccurate. Intraspecific comparisons between maps of observed angular velocity and predicted sensitivity allow identification of map types associated with different general locomotor modes and do not support existing hypotheses about the primary selective forces acting on canal morphology. The new data are used to formulate and test a novel "fast-accurate hypothesis" to explain why all vertebrates are more sensitive to rotations about some axes than others. The fast-accurate hypothesis stipulates that angular velocities presented about axes of mean sensitivity are most accurately interpreted by the brain, and that selection aligns axes of mean sensitivity with axes of habitually fast rotation because accurate perception of rapid rotations confers survival benefit. The fast-accurate hypothesis was used to predict which features of the canals should be correlated with high mean angular velocities of head movement. Novel equations that predict behavior from these newly identified canal morphologies were generated and found to outperform existing equations when tested on the original sample of 11 strepsirrhine species.</p> / Dissertation

The biomechanics and muscle activity of forward and backward slope walking was investigated in humans to gain additional insight into neural control strategies. An adjustable instrumented ramped walkway was constructed and validated. Kinematic, ground reaction force, and muscle activity data were collected from nine subjects walking at three grades (0%, 15%, and 39%) for each of four conditions (forward upslope and downslope and backward upslope and downslope). The changes observed in the data were generally progressive from 0% to 15% to 39% grade. During forward downslope walking the joint moment pattern at the knee changed significantly, power absorption increased, and changes in the muscle activity patterns corresponded directly to changes in joint mechanics. During forward upslope walking, the hip joint moment pattern changed significantly, power generation increased, and changes in the muscle activity pattern were not directly related to changes in the joint moments at all joints. The muscle activity pattern data suggest that modifications to the level walking control strategies were necessary during slope walking. Backward slope walking was used to further explore these findings. Backward upslope and forward downslope kinematics and kinetics were similar, as were those from backward downslope and forward upslope walking. However, power generation increased during upslope walking tasks and power absorption increased during downslope walking tasks, and the changes in muscle firing patterns were more similar for these tasks than for those with similar kinetics. Increased power generation required compensatory muscle activity at adjacent joints that was not directly related to the moments at those joints; increased power absorption did not require such compensatory activity, and muscle activity was directly related to the joint moments. Overall, these data suggest that changes in the control strategy and/or modifications of the level walking control strategy are strongly influenced by the power demands of a task. The characterization of forward and backward slope walking presented here is novel and has important implications for many patient populations; knowledge of the task mechanics may be used to develop or improve physical therapy and rehabilitation exercise programs as well as the design of replacement and/or assistive devices.

Even zooplankton can not be utilized directly by human being; it is an important food source for numerous economical fishes. Zooplankton¡¦s predator-prey interactions can affect not only global carbon fixation, but also fisheries yields directly. Copepods and ciliates are the targets of the current study, which act as critical links between classical diatom-copepod-fish webs and microbial food webs. Analyzing their predator-prey interactions can help us understand more about marine food production. The objective of this study is to investigate the differences in swimming behavior of copepods and ciliates under two environments, which are disturbances and no disturbances of predator-prey. We use five locomotive indicators (NGDR, turning rate, diffusion coefficient, kinetic energy and fractal dimension) to quantify swimming patterns. The trajectories of copepods in the undisturbed situation show circuitous, larger turning angle, and more diffusive behavior, which associate with a lower kinetic energy. The patterns of copepod movement with the presence of prey (ciliates) are contrary to the previous situation. The patterns of ciliates in the undisturbed situation are similar to those of copepods in undisturbed situation, except smaller turning angles. The trajectories of ciliates in terms of the turning and diffusive movement when predators (copepods) show up are different from those of copepods when preys (ciliates) are present. In addition to indicators, this study develops a new encoding scheme for accommodating the spatial-temporal information embedded in the original data. By analyzing the encoded data through some data mining techniques, the predator-prey interactive behaviors in the spatial scale can be easily perceived.

Reproduction de : Thèse de doctorat : Biomécanique : Toulon : 2007. / Titre provenant du cadre-titre. Références bibliographiques p. 193-200.

Thesis (Ph. D.)--University of Hong Kong, 2001. / Includes bibliographical references.

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2009. / Härtill 4 uppsatser.

Maneuvering through 3D structures nimbly is pivotal to the advancement of legged locomotion. However, few methods have been developed that can generate 3D gaits in those terrains and fewer if none can be generalized to control dynamic maneuvers. In this thesis, foot placement planning for dynamic locomotion traversing irregular terrains is explored in three dimensional space. Given boundary values of the center of mass' apexes during the gait, sagittal and lateral Phase Plane trajectories are predicted based on multi-contact and inverted pendulum dynamics. To deal with the nonlinear dynamics of the contact motions and their dimensionality, we plan a geometric surface of motion beforehand and rely on numerical integration to solve the models. In particular, we combine multi-contact and prismatic inverted pendulum models to resolve feet transitions between steps, allowing to produce trajectory patterns similar to those observed in human locomotion. Our contributions lay in the following points: (1) the introduction of non planar surfaces to characterize the center of mass' geometric behavior; (2) an automatic gait planner that simultaneously resolves sagittal and lateral feet placements; (3) the introduction of multi-contact dynamics to smoothly transition between steps in the rough terrains. Data driven methods are powerful approaches in absence of accurate models. These methods rely on experimental data for trajectory regression and prediction. Here, we use regression tools to plan dynamic locomotion in the Phase Space of the robot's center of mass and we develop nonlinear controllers to accomplish the desired plans with accuracy and robustness. In real robotic systems, sensor noise, simplified models and external disturbances contribute to dramatic deviations of the actual closed loop dynamics with respect to the desired ones. Moreover, coming up with dynamic locomotion plans for bipedal robots and in all terrains is an unsolved problem. To tackle these challenges we propose here two robust mechanisms: support vector regression for data driven model fitting and contact planning, and trajectory based sliding mode control for accuracy and robustness. First, support vector regression is utilized to learn the data set obtained through numerical simulations, providing an analytical solution to the nonlinear locomotion dynamics. To approximate typical Phase Plane behaviors that contain infinite slopes and loops, we propose to use implicit fitting functions for the regression. Compared to mainstream explicit fitting methods, our regression method has several key advantages: 1) it models high dimensional Phase Space states by a single unified implicit function; 2) it avoids trajectory over-fitting; 3) it guarantees robustness to noisy data. Finally, based on our regression models, we develop contact switching plans and robust controllers that guarantee convergence to the desired trajectories. Overall, our methods are more robust and capable of learning complex trajectories than traditional regression methods and can be easily utilized to develop trajectory based robust controllers for locomotion. Various case studies are analyzed to validate the effectiveness of our methods including single and multi step planning in a numerical simulation and swing foot trajectory control on our Hume bipedal robot. / text