4Ps-Plunder-Planet | Machine learning based prediction of user performance

4Ps-Plunder-Planet is a Python library. 4Ps-Plunder-Planet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Digital games combining both physical and psychological fitness and gaming, called exergames, emerged in the 1980s. Exergames promise improvements in the physical state of a player (caloric expenditure, coordination and heart rate increase), in the psychosocial state (social interaction, mood and motivation) and in the cognitive state (spatial awareness and attention). One such exergame is Plunder Planet, a dynamically-adaptive exergame developed by Martin-Niedecken and Götz. The player navigates a flying pirate ship through a desert filled with obstacles and defends himself against giant sandworms by activating a shield. The user gets points awarded by collecting crystals, and points deducted after each collision with an obstacle or a sandworm. Currently, the game difficulty can be set manually by a second person observing the user. The goal of this thesis was to create a model that predicts the in-game performance of the user, which enables to automatically adjust the difficulty to the user's physical and emotional state, allowing for a fast entry into a so-called Dual Flow, a state where the player is neither over- nor under-challenged, and thus the player can benefit form a better fitness experience. Based on log files of users playing the game, we created a machine learning model that predicts the user's in-game performance, namely whether or not the user is going to crash into the next obstacle. The modeling step consisted of analyzing and validating log files and extracting, pre-processing and selecting features. Different metrics were used to evaluate the performance of our models. We used both classical machine learning classifers such as SVM, k-Nearest Neighbor, Random Forests and Naive Bayes models, and Recurrent Neural Networks with Long Short-Term Memory units.