## 5 Results
### Evaluation of Classification Performance Using the Simulated Signals  
The discussion in the previous section has shown that, mostly on a qualitative level, our method generates signals that replicate a significant portion of the respective real sensor output within our application domain. We now proceed to quantitatively evaluate how well such simulated sensor signals are suited for training activity recognition that will later be applied to real sensor signals. The evaluation procedure has already been described in Section 4.2. Essentially we have recorded a dataset where for each activity and user we have both a video signal and sensor data. In addition, we have collected some YouTube videos for the same/similar activities. We then train classifiers using the real sensor data, simulated sensor data from the videos that we have recorded, simulated sensor data from the YouTube videos and various combinations thereof. The classifier trained on real sensor data is the one to which we compare the performance of the different combinations to evaluate the usefulness of our approach. Given the discussion in the previous section, we focus on using raw data without filtering and scaling with some examples of the impact of 8 Hz filtering. The results are shown in Figures 16-18. In each figure, we plot the recognition rate of each classifier vs. the number of users in the training set. The only exception is the case of sensor data generated from YouTube videos where we had different users perform different exercises from the set which means that the notion of \"number of users\" for the whole dataset makes no sense (see Table 1). Instead, we have generated training data from the entire 14 min of the YouTube data that we harvested and plotted it for comparison as a constant in the graphs. 1. **Baseline. ** The recognition rates on the real data reach up to 90% which, given the fact that our aim was not to optimize a recognition system for a specific application, is an acceptable baseline to evaluate the performance of the simulated sensor data. 2. **Overall performance on simulated sensor data. ** Systems trained on the simulated data reach up to 80% recognition rate, which is 10% below the results (see e.g., Figure 16 left where the acceleration norm based recognition is among the best performing variants overall). In general as the number of users is small the results for real and simulated data are very similar to the performance of the real data. The performance on the real sensor data improves faster with the amount of training data. This is to be expected as with a very small number of users (equal a small amount of training data) the recognition rate tends to be poor and limiting factor for the performance is the lack of diversity in the data and not the data quality. As the amount of data increases, the quality becomes the limiting factor, which is better for the real sensor data. 3. **Performance on YouTube data. ** The performance of the system based on sensor data simulated from YouTube videos is in most cases slightly below the performance of the baseline on a single or two users. Given that the total amount of YouTube data (around 14 min) is in the order of magnitude of the length of the data from a single user this is not surprising. 4. **Compensating deficiencies of simulated data through training set size. ** There is strong indication that deficiencies of the simulated sensor data can be compensated by the amount of such data. Thus in most cases (see discussion of different sensor combinations later on) systems based on simulated sensor data can match the performance of the real sensor data based system trained on about half as much data. Again looking at the acceleration norm from all sensors case in Figure 16 left top we see that the Mean F1 score for simulated data with 12 users is about the same as the score for 6 users with real data.