## 5 Results
### Evaluation of Classification Performance Using the Simulated Signals
Given the limitations on recognizing rotations around certain axes (in particular the limb axis) from the video signal discussed in Section 2 this was to be expected. The very poor performance of the simulated gyro signal also drags down the combined acceleration/gyro norm performance shown in the bottom right part of Figure 16. (b) _Acceleration and linear acceleration on the limb's axis._ Further looking at the impact of linear acceleration Figure 18 shows the results for raw acceleration and linear acceleration along the limb axis. The idea is that the acceleration along the axis is independent of the rotation around the axis, which, as already explained is hard to exactly capture from video. It can be seen that both have slightly lower performance than the acceleration norm (which is not surprising given that the acceleration norm is a good discriminator for our set of activitiesas described above). Within expected statistical bounds the difference between the simulated and the real sensor signal is about the same for both the raw and the linear acceleration. 3. _Different sensor placements._ In Section 5.2.2 we have already considered the use of sensors from only a wrist or only the ankle (with respect to acceleration norm) as shown in Figure 15. The performance for the real sensor data on both the wrist and the ankle and the simulated sensor data on the wrist was with around 10-20% less than for the combination which is not a surprising result (both legs and arms are relevant for our test activities). What may be surprising at first is the fact that the performance for the ankle using simulated data is half that of the real data (30-40%). The explanation is that foot motions involve a lot of hard ground impacts that lead to \"ringing\" that has already often been mentioned as something that the simulated data cannot replicate (which is why 8 Hz filtering helps a lot here). In addition vertical orientation plays a bigger role than for the wrists and there are many motions \"to the front\" (towards the camera) which are more difficult to resolve due to the viewing angle. 4. **Effects of 8 Hz Filtering. ** The effect of various pre-processing strategies has already been discussed in Section 5.2 establishing that while filtering and to some degree scaling did seem to be beneficial in some cases, overall the effect was not overwhelming given that these effects may be strongly application specific. This is why we have decided to do most of the analysis in this section on the unfiltered, not scaled signal. However, for comparison Figure 19 includes the same evaluation done in Figure 16 with 8 Hz filtering. We can see that, while for the gyro norm (which was the poorest one to start with) the filtering does indeed improve the relative performance of the simulated signal based model, it has little effect otherwise. Figure 16: Results when using the accelerometer and gyro norm and the combination thereof. Figure 17: Results when using the gyroscope and accelerometer norms and adding simulated data to the real one. Figure 18: Results when using the linear acceleration in the limb axis.