This study examined the acute effects of whole-body corrective exercise on postural alignment in a sample of 50 male participants (18-30 y) displaying asymmetrical postural deviations. All participants were randomly assigned to either a non-exercise control (n = 25) or corrective exercise treatment (n = 25) group. A three-dimensional motion analysis Vicon system was employed to quantify standing postural alignment at the beginning and end of a 6 d study. Postural misalignments were determined in degrees of symmetry (tilt) and rotation using horizontal and vertical virtual plumb lines for the following locations: hip (ASIS), leg (greater trochanter), shoulder (acromion process), and head (ear). The treatment group completed five corrective exercise sessions on separate days which included 11 exercises (requiring about 60 min per session to complete). The control group performed no intervention and maintained a normal lifestyle. At the commencement of the study there were no significant differences in the degree of postural misalignment between the control and treatment groups at any of the postural measurements. At the conclusion of the treatment period (following the five sessions of corrective exercise), there were no significant differences in any of the postural alignments of any of the postural measurements between the treatment and control groups. For example, all of the following postural measurements were not significantly different (critical F ≥ 4.24;df = 1,25) between groups: hip (ASIS) tilt (F = 0.05), hip (ASIS) rotation (F = 0.15), greater trochanter tilt (F = 1.58), greater trochanter rotation (F = 0.33), shoulder tilt (F = 2.63), shoulder rotation (F = 0.07), head tilt (F = 2.39), and head rotation (F = 2.79). The results of this study suggest that five sessions of corrective exercise were insufficient to significantly improve standing postural alignment. In addition, this study appears to be the first to document whole-body postural alignment using 3D video analysis.

The objective of this study was to investigate associations between injury-related kinematic parameters and overweight measures for youth baseball pitchers. The injury-related kinematic parameters considered were measurements 1) at foot contact: stride length, front foot position, shoulder external rotation, shoulder abduction, and elbow flexion; 2) between FC and ball release: peak knee extension; and 3) at BR: shoulder abduction. Data from three separate collection sites examined pitching mechanics of 18 10- to 11-year-old pitchers, 11 14- to 16-year-old pitchers, and 104 16- to 18-year-old pitchers Linear regression analyses were performed to determine significant correlations between kinematic parameters and body mass index (BMI) for each of the three age groups (10- to 11-year-olds, 14- to 16-year-olds, 16- to 18-year-olds). The significant findings were 1) for 10- to 11-year-old pitchers, stride length was negatively correlated with BMI and front foot position was positively correlated with BMI and 2) for 16- to 18-year-old pitchers, shoulder external rotation was negatively correlated with BMI and elbow flexion was positively correlated with BMI. A key clinical implication of this study is that select kinematic parameters have been identified that could guide coaches and trainers when working with overweight pitchers. In addition, select kinematic parameters of concern have been identified for different age ranges.

The objectives of this study were to (1) develop a non-invasive method (referred to as Smart Photo-Tape) to calculate participant-specific upper arm, forearm, and hand segment inertial properties (SIPs) (e.g. mass, center of mass, and radii of gyration) and (2) use those Smart Photo-Tape properties in inverse dynamics (ID) analyses to calculate injury-related pitching arm kinetics. Five 20- to 23- year-old baseball pitchers were photographed holding a baseball and analyzed using the Smart Photo-Tape method to obtain 3-D inertial properties for their upper arm, forearm, and hand. The upper arm and forearm segments were modelled as stacked elliptic cylinders and the hand was modelled as an ellipsoid. One participant received a dual energy x-ray absorptiometry (DXA) scan and conducted a motion analysis study, pitching 10 fastballs. Scaled SIPs from cadaver studies and Smart Photo-Tape SIPs were compared using one sample t-tests. Pitching arm kinetic predictions were calculated and compared using scaled inverse dynamics (ID), Smart Hand ID (a combination of scaled SIPs for the upper arm and forearm and Smart Photo-Tape SIPs for the hand), and Smart Photo-Tape ID. The major result was that the Smart Photo-Tape SIPs were significantly different when compared to their respective scaled inertial properties, with the hand segment producing the largest difference between the scaled SIPs and Smart Photo-Tape SIPs. The implication of this study is that researches or coaches can use the Smart Photo-Tape method to calculate participant specific SIPs for pitching arm kinetic analysis.

No description available.

No description available.

The ability to understand occlusion and disocclusion is critical in analyzing motion and forecasting changes. For example, when we see a car gradually blocks our view of a human figure, we know that either the car or the human is moving. We also know that the human behind the car will be visible again if we move to other positions. As many vision-based intelligent systems need to handle and react to visual data with potentially intensive motions, it is therefore beneficial to incorporate the occlusion reasoning into such systems. In this thesis, we study how we can improve the performance of vision-based deep learning models by harnessing the power of occlusion handling. We first visit the problem of optical flow estimation for motion analysis. We present a deep learning module that builds upon occlusion handling methods in classic Computer Vision literature. Our results show performance improvement in occluded regions on standard benchmarks, as well as real-world applications. We then examine the problem of view synthesis for 3D photography. We propose an inpainting method that leverages local color and depth context for novel view synthesis. We validate the proposed inpainting approach with a series of quantitative and qualitative experiments, and demonstrate promising results in predicting plausible content in occluded regions. / Master of Science / Human has the ability to understand occlusion, and make use of such knowledge to make predictions about motions and occluded contents. For example, when we see a car gradually blocks our view of a human figure, we know that either the car or the human is moving. We also know that the human behind the car will be visible again if we move to other positions. In this thesis, we study how we can replicate such an ability to artificial intelligence systems. We first investigate the effect of occlusion reasoning in the task of predicting motion. Our experimental results show that a system equipped with our occlusion reasoning module can better capture the motions happening in image sequences. Next, we examine the problem of hallucinating visual contents that are blocked in an image. We develop a model that can produce plausible content in occluded regions. In our experiments, we show that given one single RGB image with an estimated depth map, our model can produce a corresponding 3D photo by hallucinating the structures that are not visible in the image.

Skills assessment in Minimally Invasive Surgery (MIS) has been a challenge for training centers for a long time. The emerging maturity of camera-based systems has the potential to transform problems into solutions in many different areas, including MIS. The current evaluation techniques for assessing the performance of surgeons and trainees are direct observation, global assessments, and checklists. These techniques are mostly subjective and can, therefore, involve a margin of bias. The current automated approaches are all implemented using mechanical or electromagnetic sensors, which suffer limitations and influence the surgeon’s motion. Thus, evaluating the skills of the MIS surgeons and trainees objectively has become an increasing concern. In this work, we integrate and coordinate multiple camera sensors to assess the performance of MIS trainees and surgeons. This study aims at developing an objective data-driven assessment that takes advantage of multiple coordinated sensors. The technical framework for the study is a synchronized network of sensors that captures large sets of measures from the training environment. The measures are then, processed to produce a reliable set of individual and composed metrics, coordinated in time, that suggest patterns of skill development. The sensors are non-invasive, real-time, and coordinated over many cues such as, eye movement, external shots of body and instruments, and internal shots of the operative field. The platform is validated by a case study of 17 subjects and 70 sessions. The results show that the platform output is highly accurate and reliable in detecting patterns of skills development and predicting the skill level of the trainees.

When the primary measurement sensor is passive in nature--by which we mean that it does not directly measure range or range rate--there are well-documented challenges for target state estimation. Most estimation schemes rely on variations of the Extended Kalman Filter (EKF), which, in certain situations, suffer from divergence and/or covariance collapse. For this and other reasons, we believe that the Kalman filter is fundamentally ill-suited to the problems that are inherent in target state estimation using passive sensors. As an alternative, we propose a bounded-error (or set-membership) approach to the target state estimation problem. Such estimators are nearly as old as the Kalman filter, but have enjoyed much less attention. In this study we develop a practical estimator that bounds the target states, and apply it to the two-dimensional case of a submarine tracking a surface vessel, which is commonly referred to as Target Motion Analysis (TMA). The estimator is robust in the sense that the true target state does not escape the determined bounds; and the estimator is not unduly pessimistic in the sense that the bounds are not wider than the situation dictates. The estimator is--as is the problem itself--nonlinear and geometric in nature. In part, the simplicity of the estimator is maintained by using redundant states to parameterize the target's velocity. These redundant states also simplify the incorporation of other measurements that are frequently available to the system. The estimator's performance is assessed in a series of simulations and the results are analyzed. Extensions of the algorithm are considered.

Male crickets produce a species specific song to attract females which in response move towards the sound source. This behaviour, termed phonotaxis, has been the subject of many morphological, neurophysiological and behavioural studies making it one of the most well studied examples of acoustic communication in the animal kingdom. Despite this fact, the precise leg movements during this behaviour is unknown. This is of specific interest as the cricket’s ears are located on their front legs, meaning that the perception of the sound input might change as the insect moves. This dissertation describes a methodology and an analysis that fills this knowledge gap. I developed a semi-automated tracking system for insect motion based on commercially available high-speed video cameras and freely available software. I used it to collect detailed three dimensional kinematic information from female crickets performing free walking phonotaxis towards a calling song stimulus. I marked the insect’s joints with small dots of paint and recorded the movements from underneath with a pair of cameras following the insect as it walks on the transparent floor of an arena. Tracking is done offline, utilizing a kinematic model to constrain the processing. I obtained, for the first time, the positions and angles of all joints of all legs and six additional body joints, synchronised with stance-swing transitions and the sound pattern, at a 300 Hz frame rate. I then analysed this data based on four categories: The single leg motion analysis revealed the importance of the thoraco-coxal (ThC) and body joints in the movement of the insect. Furthermore the inside middle leg’s tibio-tarsal (TiTa) joint was the centre of the rotation during turning. Certain joints appear to be the most crucial ones for the transition from straight walking to turning. The leg coordination analysis revealed the patterns followed during straight walking and turning. Furthermore, some leg combinations cannot be explained by current coordination rules. The angles relative to the active speaker revealed the deviation of the crickets as they followed a meandering course towards it. The estimation of ears’ input revealed the differences between the two sides as the insect performed phonotaxis by using a simple algorithm. In general, the results reveal both similarities and differences with other cricket studies and other insects such as cockroaches and stick insects. The work presented herein advances the current knowledge on cricket phonotactic behaviour and will be used in the further development of models of neural control of phonotaxis.

This thesis investigates on clinical applicability of a novel sensing technology in the areas of postural steadiness and stroke assessment. The mechanically simple Distributive Tactile Sensing approach is applied to extract motion information from flexible surfaces to identify parameters and disorders of human movement in real time. The thesis reports on the design, implementation and testing of smart platform devices which are developed for discrimination applications through the use of linear and non-linear data interpretation techniques and neural networks for pattern recognition. In the thesis mathematical models of elastic plates, based on finite element and finite difference methods, are developed and described. The models are used to identify constructive parameters of sensing devices by investigating sensitivity and accuracy of Distributive Tactile Sensing surfaces. Two experimental devices have been constructed for the investigation. These are a sensing floor platform for standing applications and a sensing chair for sitting applications. Using a linear approach, the sensing floor platform is developed to detect centre of pressure, an important parameter widely used in the assessment of postural steadiness. It is demonstrated that the locus of centre of pressure can be determined with an average deviation of 1.05mm from that of a commercialised force platform in a balance application test conducted with five healthy volunteers. This amounts to 0.4% of the sensor range. The sensing chair used neural networks for pattern recognition, to identify the level of motor impairment in people with stroke through performing functional reaching task while sitting. The clinical studies with six real stroke survivors have shown the robustness of the sensing technique to deal with a range of possible motion in the reaching task investigated. The work of this thesis demonstrates that the novel Distributive Tactile Sensing approach is suited to clinical and home applications as screening and rehabilitation systems. Mechanical simplicity is a merit of the approach and has potential to lead to versatile low-cost units.