## 5 Results  
### Signal Level Evaluation  
Since the aim of our work is not to generate signals that mimic real sensor ones as exactly as possible, but to facilitate training of activity recognition systems that will later be applied to real signals, the key performance indicator is not some sort of numerical dissimilarity measure but the ability to replicate relevant, characteristic signal features that can be used for class discrimination. As the latter is difficult to quantify on signal level, we focus on the quantitative evaluation on the classification tasks with the results being presented in Sections 5.3 and 5.2. Nonetheless it is informative to have a qualitative look at the signals that our system generates for various sensors and how they compare to the real sensor signals generated in the same situation. To this end we consider situations where we have video and sensor data for the same activity. We then plot over each other the actual sensor signal (red in all figures) and the corresponding simulated sensor signal generated by our system (blue in all figures). We begin by looking at signals generated for the dataset that we used for training the regression. This is in a way the easiest task as we do not have to deal with the question of how representative the training dataset that we assembled for our regression model is. In Figure 7 we first consider examples of  
Figure 8: Architecture for the neural network used for classification. For a definition of the blocks see Figure 5. The size of the window for classification was 128, representing 2.56 s and the number of classes in our target dataset is 10.  
signals for the accelerometer and gyroscope norm \(\sqrt{x^{2}+y^{2}+z^{2}}\). This avoids problems associated with the difficulty of estimating small rotations around the limb axis which have influence in particular on the projection of the gravity vector onto the individual sensor axis. The signals are shown for a period of time of around 10 min. For each signal we show the unprocessed signal and a version filtered with 8 Hz (the impact of the filtering will be discussed later). General observations from Figure 7 are:  
1. Overall the simulated signal has the same trends and large scale features as the original signal. 2. Many of the smaller features such as distinct peaks are also matched by the simulated signal. However, some are missed or have a much smaller amplitude in the simulated signal. 3. The main difference between the simulated and the real signal is in the amplitude. In most cases the simulated amplitude is smaller, but not by a constant factor that could be overcome with simple scaling. The underestimation is particularly pronounced for high frequency peaks. This can be explained by a number of factors. First of all high frequency components (\"details\") tend to be in general more difficult to reproduce and given the limited size of our training set, some limitations in this area are not surprising. Second, in particular for the acceleration sensors, some of the peaks are due to physical effects which are not present or very difficult to capture in the video (e.g., high frequency \"ringing\" after sudden impact, see Section 2). 4. Especially in the acceleration signals there is a \"baseline shift\"-like effect (especially visible in the third row of Figure 7). At times the system seems to completely ignore parts that are below a baseline situated just below 10 (corresponding to around 1 g). This can be attributed to downward motions where the earth gravity is subtracted from the acceleration. Thus a free falling object (accelerating downwards with 1 g) experiences no acceleration force (is weightless). This means that the acceleration norm is smaller than 1 g (actually 0 in free fall), at least as experienced by the real sensor.