Baseball pitching kinematics, joint loads, and injury prevention

There is a need for the prevention of upper extremity injuries that affect a large number of competitive baseball players. Currently available evidence alludes to three possible ways to prevent these injuries: 1) regulation of unsafe participation factors, 2) implementation of exercise intervention to modify suboptimal physical characteristics, and 3) instructional intervention to correct improper pitching techniques. Of these three strategies, instruction of proper pitching technique is under-explored as a method of injury prevention. Therefore, the purpose of this review was to explore the utility of pitching technique instruction in prevention of pitching-related upper extremity injuries by presenting evidence linking pitching technique and pitching-related upper extremity injuries, as well as identifying considerations and potential barriers in pursuing this approach to prevent injuries. Various kinematic parameters measured using laboratory-based motion capture system have been linked to excessive joint loading, and thus pitching-related upper extremity injuries. As we gain more knowledge about the influence of pitching kinematics on joint loading and injury risk, it is important to start exploring ways to modify pitching technique through instruction and feedback while considering the specific skill components to address, mode of instruction, target population, duration of program, and ways to effectively collaborate with coaches and parents

A framework for correcting human motion alignment for traditional dance training using augmented reality

This paper presents a framework for motion capture analysis for dance learning technology using Microsoft Kinect V2. The proposed technology utilizes motion detection, emotion analysis, coordination analysis and interactive feedback techniques for a particular dance style selected by the trainee.This motion capture system solves the heterogeneity of the existing dance learning system and hence provides robustness. The analysis of the proposed work is carried out using query techniques and heuristic evaluation. The Microsoft Kinect V2 embedded with Augmented Reality (AR) technology is explored to demonstrate the recognition accuracy of the proposed framework

Virtual relationships: the dancer and the avatar

People might assume that dancing with a digital avatar would be a relatively distant, dehumanizing or disembodied process. However, in this article we propose that effective and creative choreographic practice can be achieved by working with a virtual representation of a dancer, and we offer two case studies to evidence the practical application of motion capture technology within this context. We observed that the virtual model quickly and naturally becomes an extension of the dancer's interiority and that a dynamic affective attunement between dancer and avatar spontaneously develops. We describe how the relationship between the physical and the virtual dancing body raises several practical, theoretical and even philosophical questions for choreographic approach, style and process. Building from Susanne Langer's (1953) germinal conception of the ‘virtual powers’ of dance, we articulate a practice-led research opportunity to critically reflect on conventional choreographic practices through the affordances of a specifically digital virtuality, in ways that can open out the kinds of affective, emotional and phenomenological frameworks within which creation occurs. The unique affordances of recent motion capture systems, offer naturalistic three-dimensional environments with an increased improvisational interactivity that simply cannot be achieved with video-based media

Explainable and Advisable Learning for Self-driving Vehicles

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly

Transforming High School Physics With Modeling And Computation

The Engage to Excel (PCAST) report, the National Research Council\u27s Framework for K-12 Science Education, and the Next Generation Science Standards all call for transforming the physics classroom into an environment that teaches students real scientific practices. This work describes the early stages of one such attempt to transform a high school physics classroom. Specifically, a series of model-building and computational modeling exercises were piloted in a ninth grade Physics First classroom. Student use of computation was assessed using a proctored programming assignment, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Student views on computation and its link to mechanics was assessed with a written essay and a series of think-aloud interviews. This pilot study shows computation\u27s ability for connecting scientific practice to the high school science classroom

Immersive Visualization in Biomedical Computational Fluid Dynamics and Didactic Teaching and Learning

Virtual reality (VR) can stimulate active learning, critical thinking, decision making and improved performance. It requires a medium to show virtual content, which is called a virtual environment (VE). The MARquette Visualization Lab (MARVL) is an example of a VE. Robust processes and workflows that allow for the creation of content for use within MARVL further increases the userbase for this valuable resource. A workflow was created to display biomedical computational fluid dynamics (CFD) and complementary data in a wide range of VE’s. This allows a researcher to study the simulation in its natural three-dimensional (3D) morphology. In addition, it is an exciting way to extract more information from CFD results by taking advantage of improved depth cues, a larger display canvas, custom interactivity, and an immersive approach that surrounds the researcher. The CFD to VR workflow was designed to be basic enough for a novice user. It is also used as a tool to foster collaboration between engineers and clinicians. The workflow aimed to support results from common CFD software packages and across clinical research areas. ParaView, Blender and Unity were used in the workflow to take standard CFD files and process them for viewing in VR. Designated scripts were written to automate the steps implemented in each software package. The workflow was successfully completed across multiple biomedical vessels, scales and applications including: the aorta with application to congenital cardiovascular disease, the Circle of Willis with respect to cerebral aneurysms, and the airway for surgical treatment planning. The workflow was completed by novice users in approximately an hour. Bringing VR further into didactic teaching within academia allows students to be fully immersed in their respective subject matter, thereby increasing the students’ sense of presence, understanding and enthusiasm. MARVL is a space for collaborative learning that also offers an immersive, virtual experience. A workflow was created to view PowerPoint presentations in 3D using MARVL. A resulting Immersive PowerPoint workflow used PowerPoint, Unity and other open-source software packages to display the PowerPoint presentations in 3D. The Immersive PowerPoint workflow can be completed in under thirty minutes

Exploring Design Opportunities for Technology-Supported Yoga Practices at Home

Department of Human Factors EngineeringYoga is a discipline that integrates mind and bodily exercises practiced for a number of health benefits. Although physical and mental health benefits from practicing yoga are well-known, people address time and cost as the primary barrier to incorporating yoga practices on a regular basis. A cost-effective solution to these limiting factors is adopting at-home practices. However, starting at-home yoga practices is difficult, especially for beginners, due to the lack of feedback on practitioners??? performance. To tackle this challenge, we explore design opportunities for an interactive artifact that can effectively support yoga practices at home that can potentially replace professional personal trainers. Our approach for exploring this design space begins with a user study with a group of yoga practitioners in order to identify design requirements in a yoga practice environment. Based on the results from the user study, we provide some design insights for developing a feedback-based artifact for yoga practice in the home environment. Then, we exemplify how suggested implications can be applied to design with an illustration of a biofeedback-based mat for yoga breathing exercises. Beyond this, we inspect how the mechanism of biofeedback for breathing can be implemented by building a low-cost respiration phase detector to evaluate the quality of breath. The results from the study on the development of phase detector show per-user classifiers can identify respiration phases with mean F-scores of 0.69 for all poses and 0.78 for the baseline pose. This is an acceptable result acknowledging numerous momentary judgments are made to identify each breathing phase. Moreover, per-user classifiers for identifying three yoga poses show promising results, which can expand the application areas of the breathing phase detector. Through this series of context-driven exploratory studies, we demonstrate approaches to investigate design opportunities for technology-supported at-home yoga.clos

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results