Matching Items (394)
ContributorsWard, Geoffrey Harris (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-18
Description
Reaching movements are subject to noise in both the planning and execution phases of movement production. Although the effects of these noise sources in estimating and/or controlling endpoint position have been examined in many studies, the independent effects of limb configuration on endpoint variability have been largely ignored. The present study investigated the effects of arm configuration on the interaction between planning noise and execution noise. Subjects performed reaching movements to three targets located in a frontal plane. At the starting position, subjects matched one of two desired arm configuration 'templates' namely "adducted" and "abducted". These arm configurations were obtained by rotations along the shoulder-hand axis, thereby maintaining endpoint position. Visual feedback of the hand was varied from trial to trial, thereby increasing uncertainty in movement planning and execution. It was hypothesized that 1) pattern of endpoint variability would be dependent on arm configuration and 2) that these differences would be most apparent in conditions without visual feedback. It was found that there were differences in endpoint variability between arm configurations in both visual conditions, but these differences were much larger when visual feedback was withheld. The overall results suggest that patterns of endpoint variability are highly dependent on arm configuration, particularly in the absence of visual feedback. This suggests that in the presence of vision, movement planning in 3D space is performed using coordinates that are largely arm configuration independent (i.e. extrinsic coordinates). In contrast, in the absence of vision, movement planning in 3D space reflects a substantial contribution of intrinsic coordinates.
ContributorsLakshmi Narayanan, Kishor (Author) / Buneo, Christopher (Thesis advisor) / Santello, Marco (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Despite the critical role that the vertebral column plays in postural and locomotor behaviors, the functional morphology of the cervical region (i.e., the bony neck) remains poorly understood, particularly in comparison to that of the thoracic and lumbar sections. This dissertation tests the hypothesis that morphological variation in cervical vertebrae reflects differences in positional behavior (i.e., suspensory vs. nonsuspensory and orthograde vs. pronograde locomotion and postures). Specifically, this project addresses two broad research questions: (1) how does the morphology of cervical vertebrae vary with positional behavior and cranial morphology among primates and (2) where does fossil hominoid morphology fall within the context of the extant primates. Three biomechanical models were developed for the primate cervical spine and their predictions were tested by conducting a comparative analysis using a taxonomically and behaviorally diverse sample of primates. The results of these analyses were used to evaluate fossil hominoid morphology. The two biomechanical models relating vertebral shape to positional behaviors are not supported. However, a number of features distinguish behavioral groups. For example, the angle of the transverse process in relation to the cranial surface of the vertebral body--a trait hypothesized to reflect the deep spinal muscles' ability to extend and stabilize the neck--tends to be greater in pronograde species; this difference is in the opposite of the direction predicted by the biomechanical models. Other traits distinguish behavioral groups (e.g., spinous process length and cross-sectional area), but only in certain parts of the cervical column. The correlation of several vertebral features, especially transverse process length and pedicle cross-sectional area, with anterior cranial length supports the predictions made by the third model that links cervical morphology with head stabilization (i.e., head balancing). Fossil hominoid cervical remains indicate that the morphological pattern that characterizes modern humans was not present in Homo erectus or earlier hominins. These hominins are generally similar to apes in having larger neural arch cross-sectional areas and longer spinous processes than modern humans, likely indicating the presence of comparatively large nuchal muscles. The functional significance of this morphology remains unclear.
ContributorsNalley, Thierra Kénnec (Author) / Kimbel, William H. (Thesis advisor) / Reed, Kaye (Committee member) / Shapiro, Liza (Committee member) / Arizona State University (Publisher)
Created2013
Description
Multi-touch tablets and smart phones are now widely used in both workplace and consumer settings. Interacting with these devices requires hand and arm movements that are potentially complex and poorly understood. Experimental studies have revealed differences in performance that could potentially be associated with injury risk. However, underlying causes for performance differences are often difficult to identify. For example, many patterns of muscle activity can potentially result in similar behavioral output. Muscle activity is one factor contributing to forces in tissues that could contribute to injury. However, experimental measurements of muscle activity and force for humans are extremely challenging. Models of the musculoskeletal system can be used to make specific estimates of neuromuscular coordination and musculoskeletal forces. However, existing models cannot easily be used to describe complex, multi-finger gestures such as those used for multi-touch human computer interaction (HCI) tasks. We therefore seek to develop a dynamic musculoskeletal simulation capable of estimating internal musculoskeletal loading during multi-touch tasks involving multi digits of the hand, and use the simulation to better understand complex multi-touch and gestural movements, and potentially guide the design of technologies the reduce injury risk. To accomplish these, we focused on three specific tasks. First, we aimed at determining the optimal index finger muscle attachment points within the context of the established, validated OpenSim arm model using measured moment arm data taken from the literature. Second, we aimed at deriving moment arm values from experimentally-measured muscle attachments and using these values to determine muscle-tendon paths for both extrinsic and intrinsic muscles of middle, ring and little fingers. Finally, we aimed at exploring differences in hand muscle activation patterns during zooming and rotating tasks on the tablet computer in twelve subjects. Towards this end, our musculoskeletal hand model will help better address the neuromuscular coordination, safe gesture performance and internal loadings for multi-touch applications.
ContributorsYi, Chong-hwan (Author) / Jindrich, Devin L. (Thesis advisor) / Artemiadis, Panagiotis K. (Thesis advisor) / Phelan, Patrick (Committee member) / Santos, Veronica J. (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2014
Description
The ability to plan, execute, and control goal oriented reaching and grasping movements is among the most essential functions of the brain. Yet, these movements are inherently variable; a result of the noise pervading the neural signals underlying sensorimotor processing. The specific influences and interactions of these noise processes remain unclear. Thus several studies have been performed to elucidate the role and influence of sensorimotor noise on movement variability. The first study focuses on sensory integration and movement planning across the reaching workspace. An experiment was designed to examine the relative contributions of vision and proprioception to movement planning by measuring the rotation of the initial movement direction induced by a perturbation of the visual feedback prior to movement onset. The results suggest that contribution of vision was relatively consistent across the evaluated workspace depths; however, the influence of vision differed between the vertical and later axes indicate that additional factors beyond vision and proprioception influence movement planning of 3-dimensional movements. If the first study investigated the role of noise in sensorimotor integration, the second and third studies investigate relative influence of sensorimotor noise on reaching performance. Specifically, they evaluate how the characteristics of neural processing that underlie movement planning and execution manifest in movement variability during natural reaching. Subjects performed reaching movements with and without visual feedback throughout the movement and the patterns of endpoint variability were compared across movement directions. The results of these studies suggest a primary role of visual feedback noise in shaping patterns of variability and in determining the relative influence of planning and execution related noise sources. The final work considers a computational approach to characterizing how sensorimotor processes interact to shape movement variability. A model of multi-modal feedback control was developed to simulate the interaction of planning and execution noise on reaching variability. The model predictions suggest that anisotropic properties of feedback noise significantly affect the relative influence of planning and execution noise on patterns of reaching variability.
ContributorsApker, Gregory Allen (Author) / Buneo, Christopher A (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Santello, Marco (Committee member) / Santos, Veronica (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2012
ContributorsBolari, John (Performer) / ASU Library. Music Library (Publisher)
Created2018-10-04
ContributorsOftedahl, Paul (Performer) / ASU Library. Music Library (Publisher)
Created2018-09-29
ContributorsMarshall, Kimberly (Performer) / Meszler, Alexander (Performer) / Yatso, Toby (Narrator) / ASU Library. Music Library (Publisher)
Created2018-09-16
Description
The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.
ContributorsAnirudh, Rushil (Author) / Turaga, Pavan (Thesis advisor) / Cochran, Douglas (Committee member) / Runger, George C. (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2016
Description
The human body is a complex system comprised of many parts that can coordinate in a variety of ways to produce controlled action. This creates a challenge for researchers and clinicians in the treatment of variability in motor control. The current study aims at testing the utility of a nonlinear analysis measure – the Largest Lyapunov exponent (1) – in a whole body movement. Experiment 1 examined this measure, in comparison to traditional linear measure (standard deviation), by having participants perform a sit-to-stand (STS) task on platforms that were either stable or unstable. Results supported the notion that the Lyapunov measure characterized controlled/stable movement across the body more accurately than the traditional standard deviation (SD) measure. Experiment 2 tested this analysis further by presenting participants with an auditory perturbation during performance of the same STS task. Results showed that both the Lyapunov and SD measures failed to detect the perturbation. However, the auditory perturbation may not have been an appropriate perturbation. Limitations of Experiment 2 are discussed, as well as directions for future study.
ContributorsGibbons, Cameron T (Author) / Amazeen, Polemnia G (Thesis advisor) / Amazeen, Eric (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2016