This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

The importance of nonverbal communication has been well established through several theories including Albert Mehrabian's 7-38-55 rule that proposes the respective importance of semantics, tonality and facial expressions in communication. Although several studies have examined how emotions are expressed and preceived in communication, there is limited research investigating the relationship between how emotions are expressed through semantics and facial expressions. Using a facial expression analysis software to deconstruct facial expressions into features and a K-Nearest-Neighbor (KNN) machine learning classifier, we explored if facial expressions can be clustered based on semantics. Our findings indicate that facial expressions can be clustered based on semantics and that there is an inherent congruence between facial expressions and semantics. These results are novel and significant in the context of nonverbal communication and are applicable to several areas of research including the vast field of emotion AI and machine emotional communication.

As threats emerge, change, and grow, the life of a police officer continues to intensify. To help support police training curriculums and police cadets through this critical career juncture, this study proposes a state of the art approach to stress prediction and intervention through wearable devices and machine learning models. As an integral first step of a larger study, the goal of this research is to provide relevant information to machine learning models to formulate a correlation between stress and police officers’ physiological responses on and off on the job. Fitbit devices were leveraged for data collection and were complemented with a custom built Fitbit application, called StressManager, and study dashboard, termed StressWatch. This analysis uses data collected from 15 training cadets at the Phoenix Police Regional Training Academy over a 13 week span. Close collaboration with these participants was essential; the quality of data collection relied on consistent “syncing” and troubleshooting of the Fitbit devices. After the data were collected and cleaned, features related to steps, calories, movement, location, and heart rate were extracted from the Fitbit API and other supplemental resources and passed through to empirically chosen machine learning models. From the results of these models, we formulate that events of increased intensity combined with physiological spikes contribute to the overall stress perception of a police training cadet