# Cycling Segments Multimodal Analysis and Classification Using Neural Networks
## 3 Results  
Figure 5 presents the analysis of the changes in the heart rate related to altitude gradients along the cycling route in segments of 10 min divided into downhill and uphill cycling, with regression coefficients 1.117 and 1.688, respectively. The method was based on a correlation analysis [19] within the selected segments. The analysis of the heart rate on flat data segments is presented in Figure 6 for two-minute data windows. Figure 6a presents the decrease of the mean heart rate in segments below an altitude of 800 m, owing to the increasing fitness level. During the 272.2 h of cycling, this mean value decreased by 4 bpm: from 104.6 to 100.6 bpm. Figure 6b presents the increase of the mean heart rate related to the mean altitude of each segment. This mean heart rate increased by 20.2 bpm from 101.2 to 121.4 bpm for altitudes ranging between 0 and 3200 m above sea level. Figure 5: The evolution of the heart rate related to the slope in selected 10-min segments: (a) downhill cycling; (b) uphill cycling. Figure 6: The evolution of the heart rate related to time in selected flat two-minute segments: (a) decrease of the heart rate in segments below an altitude of 800 m owing to the increasing fitness; (b) the increase of the mean heart rate related to altitude. Figure 7a presents the delay in the change in the heart rate with the gradient of altitude for uphill cycling in 10-min segments with a correlation coefficient greater than 0.4, and its evolution over time--its increase during the cycling route being due to the (expected) increasing fitness level of the cyclist. The regression line presents its increase from 12.8 s to 16.7 s in the time range of 270 h.  
In a similar way, Figure 7b presents that delay related to the altitude, with its decrease being caused by climate conditions and the decrease in air pressure with increasing altitude. The regression line presents its decrease from 15.3 s to 11.0 s for altitudes ranging between 0 and 3200 m above sea level. Figure 8 presents the classification of selected cycling segment features by the neural network structure specified in Figure 4 into two classes defined by cycling in areas with their mean altitude (observed and recorded by the GPS system) less than 1500 m (class \(c_{1}\)) and greater than 1500 m (class \(c_{2}\)). The mean heart rate was higher for high-altitude cycling (Figure 8a), and the mean delay between the change of the heart rate to gradient altitude was lower for cycling in higher regions (Figure 8b). The summary of mean values of selected cycling features (heart rate (HR), speed, and the delay in the change in the heart rate vs. gradient of altitude) for classification into two classes (\(c_{1}\)--mean altitude less than 1500 m and \(c_{2}\)--mean altitude greater than 1500 m) is presented in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{2}{c}{**Class 1**} & \multicolumn{2}{c}{**Class 2**} \\  & **Mean** & **STD** & **Mean** & **STD** \\ \hline HR (bpm) & 110.3 & 7.6 & 127.4 & 8.9 \\ Speed (km/h) & 14.4 & 2.8 & 13.0 & 3.2 \\ Delay [s] & 15.0 & 7.5 & 11.0 & 5.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of means and standard deviations (STD) of selected cycling features (heart rate (HR), speed, and the delay in the change in the heart rate vs. gradient of altitude) for classification into two classes (\(c_{1}\)—mean altitude less than 1500 m and \(c_{2}\)—mean altitude greater than 1500 m). Figure 7: The delay of the change in the heart rate with gradient of altitude for uphill cycling in 10-min segments and correlation coefficients larger than 0.4, presenting its evolution (**a**) in time, owing to the increasing fitness level; and (**b**) with the altitude, owing to climate conditions. The accuracy of the neural network classification was higher than 97.1% for segments having a selected length.