Filtering by
- All Subjects: Unity
- Creators: Computer Science and Engineering Program
simulated computer-generated environment. These environments are accurately simulated in that they provide the appearance of- and allow users to interact with- the simulated environment. Using head-mounted displays, controllers, and auditory feedback, virtual reality provides a convincing simulation of interactable virtual worlds (Wikipedia, “Virtual reality”). The many worlds of virtual reality are often expansive, colorful, and detailed. However, there is one great flaw among them- an emotion evoked in many users through the exploration of such worlds-loneliness.
The content in these worlds is impressive, immersive, and entertaining. Without other people to share in these experiences, however, one can find themselves lonely. Users discover a feeling that no matter how many objects and colors surround them in countless virtual worlds, every world feels empty. As humans are social beings by nature, they feel lost without a sense of human connection and human interaction. Multiplayer experiences offer this missing element into the immersion of virtual reality worlds. Multiplayer offers users the opportunity to interact with other live people in a virtual simulation, which creates lasting memories and deeper, more meaningful immersion.
Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.