## 5 Discussion
### Hug and Tail Pulling Detection Performance  
We tested the detection of hugs among other kinds of touch-based interactions. We chose to test pressing on the sides, top, and bottom because these are common ways to handle a spherical robot, and they are more similar to hugging. Even though the sensor could not provide the level of detailed tactile information that would be possible using sensors with a higher resolution, the results proved that the sensors worked well for the intended interaction (Figure 8). The highest number of false positives was obtained when pressing on the upper and lower sides. We expected this result because the poles of the sphere contained eight sensors in an area that could be covered by the hand.  
To better evaluate the accuracy of the system, precision, recall, and F-value were calculated. The results showed that the system is accurate and sensitive in detecting hugs. However, it is important to understand the potential implications related to failures in the detection. One type of error occurs when the system fails on detecting an actual hug (false negative). Among the two types of errors, this is the easiest to address because users can realize when it occurs based on the lack of response from the system (e.g., not receiving feedback right after hugging), and then correct it by hugging again. The other type of error is when different types of gestures are incorrectly detected as hugs (false positive). Unintended hug detection should be avoided as it might impact the user's credibility on the system. This sensor was designed especially for this interface, with the criteria of using soft materials and reducing the sensor complexity as much as possible. We are satisfied with the results so far as we were able to detect hugging using only eight electrodes while maintaining high performance. These values could be improved by adding different kinds of sensors (like sound or temperature) to complement the pressure sensor readings. A limitation of the current approach is related to the way the sensors are mapped into a two-dimensional polygon. This makes it impossible, for example, to detect hugs if the robot is tilted 90 degrees. Pepita was designed with features such as eyes, and decoration on the top, to give the user an idea of the orientation to hold the robot, but solutions in the future should tackle this issue. Moreover, for the current implementation, we only used spatial information at each time frame (the area of the polygons generated when the sensors are pressed), and the temporal information was neglected. Therefore, using a model which involves temporal features or past events such as Hidden Markov Model or Recurrent Neural Network would improve the accuracy of detection, and it should be considered for future implementations.  
Pulling the tail was used as a gesture that conveys a negative meaning. The sensor was designed to work with the structure and material of the tail. The magnet and the hall effect sensor were placed at a distance that resulted in clear signal changes when the tail was being pulled. To test the detection performance, we chose three other gestures that could be wrongly detected as pulling. The results of the sensor performance show that the detection was accurate enough for the proposed application (higher than 0.8). This approach offers a feasible solution with a sensor of simple structure that supports detection of pulling.