1,091 research outputs found

    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    Southern Adventist University Undergraduate Catalog 2022-2023

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2022-2023.https://knowledge.e.southern.edu/undergrad_catalog/1121/thumbnail.jp

    Deep Generative Modelling of Human Behaviour

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    Human action is naturally intelligible as a time-varying graph of connected joints constrained by locomotor anatomy and physiology. Its prediction allows the anticipation of actions with applications across healthcare, physical rehabilitation and training, robotics, navigation, manufacture, entertainment, and security. In this thesis we investigate deep generative approaches to the problem of understanding human action. We show that the learning of generative qualities of the distribution may render discriminative tasks more robust to distributional shift and real-world variations in data quality. We further build, from the bottom-up, a novel stochastically deep generative modelling model taylored to the problem of human motion and demonstrate many of it’s state-of-the-art properties such as anomaly detection, imputation in the face of incomplete examples, as well as synthesis—and conditional synthesis—of new samples on massive open source human motion datasets compared to multiple baselines derived from the most relevant pieces of literature

    Northeastern Illinois University, Academic Catalog 2023-2024

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    https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp

    Data simulation in deep learning-based human recognition

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    Human recognition is an important part of perception systems, such as those used in autonomous vehicles or robots. These systems often use deep neural networks for this purpose, which rely on large amounts of data that ideally cover various situations, movements, visual appearances, and interactions. However, obtaining such data is typically complex and expensive. In addition to raw data, labels are required to create training data for supervised learning. Thus, manual annotation of bounding boxes, keypoints, orientations, or actions performed is frequently necessary. This work addresses whether the laborious acquisition and creation of data can be simplified through targeted simulation. If data are generated in a simulation, information such as positions, dimensions, orientations, surfaces, and occlusions are already known, and appropriate labels can be generated automatically. A key question is whether deep neural networks, trained with simulated data, can be applied to real data. This work explores the use of simulated training data using examples from the field of pedestrian detection for autonomous vehicles. On the one hand, it is shown how existing systems can be improved by targeted retraining with simulation data, for example to better recognize corner cases. On the other hand, the work focuses on the generation of data that hardly or not occur at all in real standard datasets. It will be demonstrated how training data can be generated by targeted acquisition and combination of motion data and 3D models, which contain finely graded action labels to recognize even complex pedestrian situations. Through the diverse annotation data that simulations provide, it becomes possible to train deep neural networks for a wide variety of tasks with one dataset. In this work, such simulated data is used to train a novel deep multitask network that brings together diverse, previously mostly independently considered but related, tasks such as 2D and 3D human pose recognition and body and orientation estimation

    2023-2024 Lindenwood University Undergraduate Course Catalog

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    Lindenwood University Undergraduate Course Catalog.https://digitalcommons.lindenwood.edu/catalogs/1209/thumbnail.jp

    2023-2024 academic bulletin & course catalog

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    University of South Carolina Aiken publishes a catalog with information about the university, student life, undergraduate and graduate academic programs, and faculty and staff listings

    Learning Motion Skills for a Humanoid Robot

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    This thesis investigates the learning of motion skills for humanoid robots. As groundwork, a humanoid robot with integrated fall management was developed as an experimental platform. Then, two different approaches for creating motion skills were investigated. First, one that is based on Cartesian quintic splines with optimized parameters. Second, a reinforcement learning-based approach that utilizes the first approach as a reference motion to guide the learning. Both approaches were tested on the developed robot and on further simulated robots to show their generalization. A special focus was set on the locomotion skill, but a standing-up and kick skill are also discussed. Diese Dissertation beschäftigt sich mit dem Lernen von Bewegungsfähigkeiten für humanoide Roboter. Als Grundlage wurde zunächst ein humanoider Roboter mit integriertem Fall Management entwickelt, welcher als Experimentalplatform dient. Dann wurden zwei verschiedene Ansätze für die Erstellung von Bewegungsfähigkeiten untersucht. Zu erst einer der kartesische quintische Splines mit optimierten Parametern nutzt. Danach wurde ein Ansatz basierend auf bestärkendem Lernen untersucht, welcher den ersten Ansatz als Referenzbewegung benutzt. Beide Ansätze wurden sowohl auf der entwickelten Roboterplatform, als auch auf weiteren simulierten Robotern getestet um die Generalisierbarkeit zu zeigen. Ein besonderer Fokus wurde auf die Fähigkeit des Gehens gelegt, aber auch Aufsteh- und Schussfähigkeiten werden diskutiert

    Exploring Virtual Reality and Doppelganger Avatars for the Treatment of Chronic Back Pain

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    Cognitive-behavioral models of chronic pain assume that fear of pain and subsequent avoidance behavior contribute to pain chronicity and the maintenance of chronic pain. In chronic back pain (CBP), avoidance of movements often plays a major role in pain perseverance and interference with daily life activities. In treatment, avoidance is often addressed by teaching patients to reduce pain behaviors and increase healthy behaviors. The current project explored the use of personalized virtual characters (doppelganger avatars) in virtual reality (VR), to influence motor imitation and avoidance, fear of pain and experienced pain in CBP. We developed a method to create virtual doppelgangers, to animate them with movements captured from real-world models, and to present them to participants in an immersive cave virtual environment (CAVE) as autonomous movement models for imitation. Study 1 investigated interactions between model and observer characteristics in imitation behavior of healthy participants. We tested the hypothesis that perceived affiliative characteristics of a virtual model, such as similarity to the observer and likeability, would facilitate observers’ engagement in voluntary motor imitation. In a within-subject design (N=33), participants were exposed to four virtual characters of different degrees of realism and observer similarity, ranging from an abstract stickperson to a personalized doppelganger avatar designed from 3d scans of the observer. The characters performed different trunk movements and participants were asked to imitate these. We defined functional ranges of motion (ROM) for spinal extension (bending backward, BB), lateral flexion (bending sideward, BS) and rotation in the horizontal plane (RH) based on shoulder marker trajectories as behavioral indicators of imitation. Participants’ ratings on perceived avatar appearance were recorded in an Autonomous Avatar Questionnaire (AAQ), based on an explorative factor analysis. Linear mixed effects models revealed that for lateral flexion (BS), a facilitating influence of avatar type on ROM was mediated by perceived identification with the avatar including avatar likeability, avatar-observer-similarity and other affiliative characteristics. These findings suggest that maximizing model-observer similarity may indeed be useful to stimulate observational modeling. Study 2 employed the techniques developed in study 1 with participants who suffered from CBP and extended the setup with real-world elements, creating an immersive mixed reality. The research question was whether virtual doppelgangers could modify motor behaviors, pain expectancy and pain. In a randomized controlled between-subject design, participants observed and imitated an avatar (AVA, N=17) or a videotaped model (VID, N=16) over three sessions, during which the movements BS and RH as well as a new movement (moving a beverage crate) were shown. Again, self-reports and ROMs were used as measures. The AVA group reported reduced avoidance with no significant group differences in ROM. Pain expectancy increased in AVA but not VID over the sessions. Pain and limitations did not significantly differ. We observed a moderation effect of group, with prior pain expectancy predicting pain and avoidance in the VID but not in the AVA group. This can be interpreted as an effect of personalized movement models decoupling pain behavior from movement-related fear and pain expectancy by increasing pain tolerance and task persistence. Our findings suggest that personalized virtual movement models can stimulate observational modeling in general, and that they can increase pain tolerance and persistence in chronic pain conditions. Thus, they may provide a tool for exposure and exercise treatments in cognitive behavioral treatment approaches to CBP

    DefaultVR: the AI Expansion. An application of artificial intelligence in competitive gaming and virtual reality.

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    This works presents the development of DefaultVR: The AI Expansion, an expansion of the first degree thesis DefaultVR, a virtual reality tactical shooter online game. In particular, this expansion aims to include an artificial agent, called Eve, capable of learning to play in the virtual reality world through reinforcement learning techniques. The agent learns to navigate the game environment, make decisions based on pseudo-visual information, and optimize its actions to maximize rewards. The development utilizes a deep reinforcement learning framework with the Proximal Policy Optimization algorithm included with Units’s ML-Agents. Extensive experiments were conducted to evaluate the agent’s performance, comparing it against itself and human players. The results demonstrate the agent’s ability to adapt and improve over time, achieving competitive gameplay skills comparable to both new and experienced human VR players. The training process involved iterative optimization and analysis of various hyperparameters, observations’ and actions’ spaces, and training configurations. The successful development of the artificial agent has significant implications for the field of gaming AI, showcasing its potential for creating engaging and challenging gameplay experiences. The research contributes to the broader understanding of reinforcement learning techniques and their application in training intelligent agents for real-world tasks
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