## 3 Results
### Classifier Performance with Different Feature Sets  
To evaluate how the feature selection methods and the number of features used for classification affect the classifier performance, the results were averaged over different sampling frequencies and window lengths while using different feature sets (Figure 6). The feature sets of 110 features, 43 features, 28 features and 13 features, achieved with the first feature selection scheme, had similar average F1-scores between 0.89 and 0.90. The SFS feature set had a slightly higher average F1-score of 0.92 \(\pm\) 0.03.  
Figure 4: F1-scores of different activity types (mean \(\pm\) SD (Standard deviation)) averaged over sampling frequencies and feature sets using different window lengths. Asterisks show significant statistical difference between different values of the window length (\(p\)\(<\) 0.05).  
Figure 5: F1-score of different activity types (mean \(\pm\) SD) averaged over window lengths and feature sets using different sampling frequencies. Asterisks show a significant statistical difference between different values of the sampling frequency (\(p\)\(<\) 0.05).  
The SFS feature set had a major increase in performance compared to other feature sets classifying outdoor cycling (\(0.94\pm 0.04\) compared to an average of \(0.86\pm 0.09\) with other sets) and a slight increase in classifying low intensity activity type (\(0.90\pm 0.04\) compared to an average of \(0.86\pm 0.04\)).  
Since both classification window length and sampling frequency of the acceleration signal affect the number of samples in classification fragments, it is important to evaluate their combined effect on classification performance. Figure 7 shows the average classification F1-scores with different feature sets using different combinations of sampling frequencies and window lengths. The SD values were large, since the results were averaged over different activity types with different F1-scores.  
The average F1-scores of all the combinations of sampling frequencies and window lengths were similar to all of the feature sets of the first feature selection scheme. The classification performance was better with combinations that had more samples per classification fragment, with the highest average of \(0.93\pm 0.05\) achieved with the combination of \(50\) Hz and \(5\) s. The results with the combinations that had either \(1\) s window length or sampling frequency of \(13\) Hz were lower compared to other combinations with most feature sets.  
Compared to the feature sets of the first feature selection scheme, the SFS method used in the second scheme had higher performance with most window length and sampling frequency combinations. This difference was very noticeable with \(13\) Hz sampling frequency. The number of features used in  
Figure 6: F1-scores of different activity types (mean \(\pm\) SD) averaged over window lengths and sampling frequencies using different feature sets.  
Figure 7: F1-scores (mean \(\pm\) SD) averaged over all activities using different feature sets, window lengths and sampling frequencies.  
SFS feature sets was between 9 and 14 (Table 3), being remarkably lower than the number of features in most of the feature sets achieved with the first feature selection scheme.