Web based Data visualization for an Immersive 3D Therapy Game for Treating Hemispatial Neglect

Neglect affects an estimated one in four individuals who experience a stroke. Because of its association with overall stroke severity, individuals with neglect tend to have poorer prognosis for recovery. Treatment of neglect in acute stroke can yield greater recovery of a person’s ability to successfully perform activities of daily living. However, most individuals have insufficient access to effective treatments. Hemi-spatial neglect is a type of brain problem after stroke that people with this problem is living in a one-side world, which means they can only recognize one side of the object they see in their eyes, and automatically ignored the other side. Current existed treatment which is called Constraint Induced Movement Therapy (CI therapy) is to Hemi-spatial neglect is having the patient staying at hospital and repeat being forced to look at the side they tend to ignore by a doctor. This traditional treatment for hemi-spatial neglect is very tedious and a repetition of a motor practice thus has little effect on patients. Recently, game-based treatments for neglect have shown some important progress. We, as a team, designed an immersive 3D video game, which has very user-friendly interface and driven by eye gaze, in order to train them to look at the other side so that they may get a better chance to recover as normal people. This will provide direct, intensive, and implicit training of visual attention, while enabling real-time assessment of performance and feedback. The game is calibrated with The Eye Tribe Eye Tracker as a monitor to the movement of eyeballs and based on 3D modeling technology: Autodesk Maya with the assistance of Adobe Photoshop for creating game assets, and imported them to the Unreal Game Engine 4.0 and program the game with C++ and BluePrint visualized language.No embargoAcademic Major: Electrical and Computer Engineerin

From Holy German Art to Degenerate Art: Nazi Ideology and Opera

Senior Project submitted to The Division of Languages and Literature of Bard Colleg

Air Pollution and Mental Health of Older Adults in China

In this paper, we explore the association between air pollution and the mental health and depression of older adults in China. Along with the rapid economic development, concerns about air pollution and recognition of the importance of mental health have risen remarkably in China. Although no firm evidence of an association between air pollution and overall mental health has been found, the results show significant evidence of a positive relationship between air pollution and depression. Moreover, we observe the presence of concerns about environmental inequality, as people are more sensitive to contaminations caused by pollutants with high variation in densities across counties, such as PM2.5, PM10, and SO2. Although O3 has a high average absolute density, the impact on mental health is low due to the limited variations nationwide. Physical fitness, gender, relative income, marital status, and social contacts are also found to be related to mental health and depression of older adults

Biomechanical Risk Assessment of Non-Contact Anterior Cruciate Ligament Injury in Taekwondo Athletes

Non-contact anterior cruciate ligament (ACL) injury can occur in many sports. It is interrelated with gender, anatomy, biomechanics, and neuromuscular control. Taekwondo athletes have a higher incidence of ACL injury than athletes from other sports. Objective: This study aimed to determine the biomechanical gender differences and mechanism of taekwondo athletes with ACL injury. Methods: A total of 28 taekwondo athletes (aged 14–19 years) were randomly selected and grouped by gender. Feet high floor, one foot high floor, and single leg squat were analyzed by a Vicon motion analysis system and Kistler 3D force platform for action. The knee joint angle and ground force were evaluated. Results: Results demonstrated biomechanical differences in knee joint between male and female athletes. Conclusion: ACL injury in taekwondo female athletes indicated the biomechanical mechanism of the knee joint, and it can be prevented by neuromuscular control training

An Analysis of the Allergy Comments on Twitter Using Data Mining Approach

Allergies are one of the most common chronic illnesses in the world. The prevalence of social media allows people to express their opinions and exchange information including symptoms of personal health. Mining those publicly accessible health-related data on social media, such as Twitter, offers a unique approach to get valuable healthcare insights. In this paper, a multi-component data mining framework was developed to collect Twitter data, detect time series patterns, discover topics of interest about allergies, and analyze the contents of tweets. From the extracted 2.2 million tweets in 2019, my experimental results show that allergy-related tweet volume is strongly correlated to the pollen data (r = .699, p < .01). Also, 152 unique topics are identified with a -28.36 perplexity score and a .67 coherence score. Furthermore, many linguistic dimensions such as the sentiment are analyzed to learn about the tweet contents. I consider this to be one of the many studies examining a large-scale social media stream to deeply analyze allergy activities. And with the growing social media, publicly available data such as Twitter posts can be used to support healthcare practitioners and social scientists in better understanding common public opinions, not just allergies.Master of Scienc

Discovering Predictable Latent Factors for Time Series Forecasting

Modern time series forecasting methods, such as Transformer and its variants, have shown strong ability in sequential data modeling. To achieve high performance, they usually rely on redundant or unexplainable structures to model complex relations between variables and tune the parameters with large-scale data. Many real-world data mining tasks, however, lack sufficient variables for relation reasoning, and therefore these methods may not properly handle such forecasting problems. With insufficient data, time series appear to be affected by many exogenous variables, and thus, the modeling becomes unstable and unpredictable. To tackle this critical issue, in this paper, we develop a novel algorithmic framework for inferring the intrinsic latent factors implied by the observable time series. The inferred factors are used to form multiple independent and predictable signal components that enable not only sparse relation reasoning for long-term efficiency but also reconstructing the future temporal data for accurate prediction. To achieve this, we introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics via the powerful deep latent dynamics models to infer the predictable signal components. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting. The statistical analysis validates the predictability of the learned latent factors