## 7 Results and Discussion
### Activity Classifier  
The following classes were included in the activity recognition:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{true}\backslash\mathbf{decision}\) & **Off**. & **Nat**. & **Str**. & **Home** & **Res**. \\ \hline
**Off**. & \(75\) & \(0\) & \(2\) & \(7\) & \(17\) \\
**Nat**. & \(0\) & \(67\) & \(21\) & \(5\) & \(8\) \\
**Str**. & \(1\) & \(9\) & \(72\) & \(6\) & \(12\) \\
**Home** & \(3\) & \(5\) & \(15\) & \(62\) & \(15\) \\
**Res**. & \(7\) & \(0\) & \(21\) & \(0\) & \(73\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix of five environment classes _office_, _nature_, _street/road_, _home_ and _restaurant/pub/cafe_ using MAP classifier. The values denote percentages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{true}\backslash\mathbf{decision}\) & **Off**. & **Nat**. & **Str**. & **Home** & **Res**. \\ \hline
**Off**. & \(70\) & \(0\) & \(1\) & \(20\) & \(10\) \\
**Nat**. & \(2\) & \(65\) & \(20\) & \(6\) & \(7\) \\
**Str**. & \(2\) & \(12\) & \(62\) & \(11\) & \(13\) \\
**Home** & \(4\) & \(2\) & \(9\) & \(77\) & \(8\) \\
**Res**. & \(2\) & \(1\) & \(26\) & \(4\) & \(68\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrix of five environment classes _office_, _nature_, _street/road_, _home_ and _restaurant/pub/cafe_ using the decision tree classifier. The values denote percentages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{true}\backslash\mathbf{decision}\) & **Off**. & **Nat**. & **Str**. & **Home** & **Res**. \\ \hline
**Off**. & \(87\) & \(0\) & \(0\) & \(5\) & \(8\) \\
**Nat**. & \(2\) & \(36\) & \(26\) & \(23\) & \(13\) \\
**Str** & \(3\) & \(13\) & \(56\) & \(8\) & \(20\) \\
**Home** & \(2\) & \(5\) & \(5\) & \(73\) & \(16\) \\
**Res**. & \(18\) & \(3\) & \(8\) & \(7\) & \(65\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Confusion matrix of five environment classes _office_, _nature_, _street/road_, _home_ and _restaurant/pub/cafe_ using the support vector machine classifier. The values denote percentages.  
* table; phone is placed on a non-moving surface
* standing; phone is in the hand or pocket, but the user is not walking or running
* walking; the user is walking
* running; the user is running
* bicycling; the user is bicycling
* vehicles; the user is driving or traveling with a motorized vehicle  
It is quite clear that the GPS speed would be a very important feature for this kind of classification. However, reliable speed information is not available all the time, so the activity classifier was implemented with two different modes, depending on the availability of the GPS [30].  
In this kind of activity classifier, the classes _table_, _standing_, and _vehicles_ may overlap in some cases. Reason for this is that the variance of the accelerometer data norm, which is usually the most dominant feature in the activity classifier, can be similar in some cases. For example, a car stopped in traffic lights or a person standing still can be confused as the class on _table_. Nevertheless, if we also use features from the GPS (GPS speed) and causal information based on, _e.g._, markov chain modelling of the classification outputs ([31]), the separation of a car stopped in traffic lights and standing can usually be done.  
The original data set did not contain enough data for the results with the activity classifier when the GPS speed is available. Thus, data were generated where the standard deviations (std) and means of the GPS speeds for each activity were chosen manually. This made it impossible to perform the decision tree classification but enabled us to calculate the adaptation results also for the activity classifier. The values selected are shown in Table 10. It should be noted that the values are naturally changing depending on the user. However, our adaptation algorithm can scale these values for the individual use.