# Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors
## 4 Results
### The Impact of Different Combinations of Sensors  
It is an interesting and instructive study on which sensors and how to combine the sensors to effectively identify the user's travel modes. At present, there have been studies using a variety of sensors for travel mode recognition [26; 27; 28]; however, these studies fail to answer the question of the effectiveness of sensor combinations. Therefore, the combinatorial effectiveness between the adopted sensors is investigated in this paper.  
In the experimental analysis, we evaluated the performance under different combinations of the sensors to discuss the impact on travel mode detection. We combine the four sensors used in this paper from a quantity of 1 to a quantity of 4. The results are shown in Table 3.  
From the results in Table 3, we can know that the more types of sensors combined, the better the result. Specifically, combining the accelerometer, gyroscope, magnetometer, and barometer readings achieves the best accuracy (98.5% measured by F-measure). The combination of the accelerometer, gyroscope and magnetometer outperforms other combinations of three types of sensors. For combinations of two types of sensors, the combination of accelerometer and gyroscope achieves the best accuracy. When considering the individual sensors used, the accelerometer achieves a better recognition performance than the other sensors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Sensors** & **Sensors** & **F-Measure** \\ \hline \multirow{4}{*}{1} & Acc. & **0.978** \\  & Gyro. & 0.914 \\  & Mag. & 0.852 \\  & Baro. & Nan \\ \hline \multirow{4}{*}{2} & Acc. + Gyro. & **0.984** \\  & Acc. + Mag. & 0.983 \\  & Acc. + Baro. & 0.973 \\  & Gyro. + Mag. & 0.916 \\  & Gyro. + Baro. & 0.812 \\  & Mag. + Baro. & 0.867 \\ \hline \multirow{4}{*}{3} & Acc. + Gyro. + Mag. & **0.984** \\  & Acc. + Mag. + Baro. & 0.983 \\ \cline{1-1}  & Acc. + Gyro. + Baro. & 0.979 \\ \cline{1-1}  & Gyro. + Mag. + Baro. & 0.915 \\ \hline \multirow{4}{*}{4} & Acc. + Gyro. + Mag. + Baro. & **0.985** \\ \cline{1-1}  & Acc. + Gyro. + Mag. + Baro. & **0.985** \\ \cline{1-1} \cline{2-4}  & Acc. + Gyro. + Mag. + Baro. & **0.986** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of different sensors.  
Figure 6: The performance under different hyperparameters (**a**): number of convolutional layers; (**b**): number of feature maps; (**c**): filter size; (**d**): pooling size; (**e**): batch size; and (**f**): learning rate.  
We also discussed the impact of different sensors fusion on accuracy under each type of travel mode. The result was shown in Figure 7. It can be seen that the combination of the accelerometer and the gyroscope can significantly improve the classification performance of Bike, Bus, Car and Walk compared to the results using only the accelerometer. The performance of Bus and Car were further improved after the magnetometer was considered. The barometer increased the recognition precision of Bus.  
For several travel modes, the more sensors combination did not work better. Such as Car, the three combinations of accelerometer, gyroscope, and magnetometer achieved the best performance, conversely, the combination of four sensors reduces the accuracy of travel mode detection. In most cases, the combination of four sensors can achieve better performance.