## 3 Method
### Datasets for Training the Regression  
Our aim is to train a model that can generate simulated sensor data not just for a single specific set of activities but for a broader domain. Thus having our regression model, application developers should be able to use our model to generate the required training data from online videos for any activity set they are interested in (as long as it is within the broad general domain). The idea is thus to train the regression model on a set of motions that are representative \"basic components\" of the activities of the respective broader domain. The core question is how to define the \"broader domain\" and how to identify the corresponding \"basic components\". For this work we have selected aerobic-like physical training. It is a very broad domain with a great variety of different exercises for which online videos are extremely popular. In terms of applications the ability to monitor the exercises is something that current fitness trackers lack, despite the popularity of such exercises and the associated potentially large user base. In technical terms the domain has the advantage that the corresponding videos tend to provide a stable, easy to process frontal perspective with the relevant parts being clearly visible most of the time (as the whole purpose of the videos is to show the viewers how to move). The activities tend to be characterized by distinct motion of the limbs and posture changes which means that recognition systems should be reasonably robust with respect to sensor data noise and inaccuracy.  
With respect to the \"basic components\" of the motions we first started by showing the participants example videos of the domain and asking them to perform random motions related to those videos. This approach has not produced good results, however, as people tended to repeat the same, not necessarily representative motions. We then turned to high school video material explaining the degrees of freedom of human joints and the types of motions that different muscles can actuate. We combined parts of 7 such videos into an 11 minute compilation [46] and had 8 subjects re-enact the motions from the videos while wearing sensors on the locations for which we wanted to train our regression model (wrists and lower legs). We filmed each user on a different day and our camera placement and angle changed slightly across recordings. Examples of the recordings are shown in Figure 6. For sensor synchronization we begin the recording by holding all IMUs stacked on top of each other and moving them together up and down in front of the camera. After the synchronization gesture is performed at least 3 times, we start recording the generic motions. During a user session, subjects were instructed to try to follow in real time the motions present in our compilation video, which is playing in a monitor nearby. In order to increase the variability of motions, we told them that it was more important to move than to perform the motions correctly. For example, when quick motions happen in the videos, it is better to move quickly, even if one cannot follow the specific quick motion. Only one of the subjects performed those seed motions more than once. His second session was selected for validation, while all others were selected for training the regression.