# Learning and Mining Player Motion Profiles in Physically Interactive Robogames
## 5 Materials and Methods
### Game Scenario  
For the present work, the playground is a rectangular area of 4 m \(\times\) 2 m, where on each corner, tubes representing towers are placed (see Figure 10). Each tower is equipped with a button and four Light Emitting Diodes (LEDs) that can be progressively turned on. Each tower LED requires the tower button to be pushed for about 2.5 s to turn on, meaning that a tower takes about 10 s to light up all four of its LEDs. Such LEDs are used as a representation of the progress of the human player in capturing a specific tower. After turning on all LEDs of a particular tower during game play, it is said that the player has captured it. Button pressing time is cumulative and can be distributed for different moments; this means that the player will not lose his/her progress if he/she stops pushing the button before a tower is captured. Each tower has a circuit, which transmits its status to the robot. In order to win, the human player must be able to secure all the existing towers without letting a single one be knocked down by the robot. If, at anytime, a tower falls (because of the robot or player), the game ends, and the human player loses. Since the robot is a holonomic base, it is able to move across the entire playground just as the human player, and it is only constrained by the fact that an already captured tower, or one whose button is currently being pushed by the player, cannot be teared down. The player can also block the robot path by staying on it. Notice that, while the player is trying to capture a given tower, the robot can try to tear down another one. By relying on the laser scans, the robot can also perceive the human player during the game, so as to avoid hitting him/her while moving. Figure 11 summarizes the overall idea of the game. When playing the game, we ask the human player to wear a colored role in order to allow for blob detection and user tracking. These tasks are carried out by the use of a Microsoft Kinect(r) sensor. In the future, we plan to study the extraction of behavior features using such device, which may potentially help to understand gameplay in a more fine-grained way. We have used a custom accelerometer board (see Figure 12) attached to the player's chest in order to capture detailed player motion information. The choice of the accelerometer position was conditioned by the need to avoid the introduction of noise from irrelevant player motion. With the sensor placed on the chest, we aimed at getting a more stable reference for motion in comparison to, for instance, data gathered from the sensor placed on the arm or thigh [13]. The custom accelerometer module is based on the InvenSense MPU-6050 three-axis accelerometer board and an Arduino Uno micro-controller (see Figure 12). The circuit also contains a Nrf24l01 radio-frequency module that allows accelerometer and tower data to be sent to the on-board computer. Using the dataset from previous work [13; 14], we considered 29 matches involving 15 male participants of different ages. The age distribution consisted of children (7-10) and adults (26-40). Recorded matches had a minimum duration of about 40 s and a maximum of about 1 min and 10 s. The collected data correspond to acceleration values along the \(x\)-, \(y\)- and \(z\)-axis with a sampling frequency of 50 Hz, which is five-times larger than the frequency considered to be sufficient for detecting daily activities from accelerometer data (10 Hz) [32; 33; 34]. In our experiments, we have also decided to use univariate accelerometer data instead of the multivariate three-axis information. For that, we took a linear combination of the axis favoring the \(z\)-axis, the one pointing forward from the player. The justification for this is that from our observations, very rarely, the player would run sideways or jump. Therefore, with the linear combination, we intend to collapse the multivariate axis information into a univariate signal that encompasses all the motion information more compactly; this makes the analysis easier to perform.