Physical appearance is an important communication tool that involves the way people define themselves and others around them. Clothing is a major component of physical appearance. When people can understand how different clothing styles flatter their specific body shapes, they can use clothing as a means to manipulate their physical appearances. In this way, people can choose deliberate clothing to send out purposeful visual messages that align with their goals and interests. Understanding how clothing affects visual appearance can be a very complicated task (Ilnitsky, 2018; Nix-Rice, 2014). There should be a simple and way for people to pick clothes that will specifically look good on their body. The purpose of this current study is to introduce a new method of online shopping that collects users’ body dimensions and shows them clothing that will flatter their particular body shape. The following study proposes a prototype design for such a shopping experience.

Affective video games are still a relatively new field of research and entertainment. Even
so, being a form of entertainment media, emotion plays a large role in video games as a whole.
This project seeks to gain an understanding of what emotions are most prominent during game
play. From there, a system will be created wherein the game will record the player’s facial
expressions and interpret those expressions as emotions, allowing the game to adjust its difficulty
to create a more tailored experience.
The first portion of this project, understanding the relationship between emotions and
games, was done by recording myself as I played three different games of different genres for
thirty minutes each. The same system that would be used in the later game I created to evaluate
emotions was used to evaluate these recordings.
After the data was interpreted, I created three different versions of the same game, based
on a template created by Stan’s Assets, which was a version of the arcade game Stacker. The
three versions of the game included one where no changes were made to the gameplay
experience, it simply recorded the player’s face and extrapolated emotions from that recording,
one where the speed increased in an attempt to maintain a certain level of positive emotions, and
a third where, in addition to increasing the speed of the game, it also decreased the speed in an
attempt to minimize negative emotions.
These tests, together, show that the emotional experience of a player is heavily dependent
on how tailored the game is towards that particular emotion. Additionally, in creating a system
meant to interact with these emotions, it is easier to create a one-dimensional system that focuses
on one emotion (or range of emotions) as opposed to a more complex system, as the system
begins to become unstable, and can lead to undesirable gameplay effects.

The purpose of this applied project was to research potential methods for conducting performance and evaluation observations on users of Positive Train Control (PTC) and recommend the most effective measures of performance (MOPs) and measures of efficiency (MOEs) of those users. I conducted a study to collect and analyze what data could be observed and examined most effectively to produce causal explanations of behaviors when utilizing the PTC system. This study was done through literature review, interviews of PTC users and trainers, and through direct observations as I rode on trains watching crews interact with the system. Additionally, I researched several studies on human computer interface (HCI) usability studies of various software applications. Based upon the results, I recommend that direct-participant observations be employed and apply both the system and individual MOPs and MOEs identified in the report to track user’s proficiency. The data collected from these observations can be centralized and used to identify behavioral trends, drive corrective actions, create future policies as well as training content. These observations will address the need to have structured observations which allow observers to focus undistracted on the specific behaviors that affect train operations. This database would also identify employees that may need additional or refresher training.