## 4 Experiment Results
### Accuracy Estimation of DMDD  
In this work, a novel data and model dual-driven model is proposed in order to solve the problems of changeable handheld modes, low-accuracy of speed estimation, and heading drift. The dataset is the fundamental cornerstone of data-driven-based pedestrian dead reckoning. In this paper, the high-precision simultaneous localization and mapping (SLAM) system is used to collect the ground-truth speed and heading information, and the smartphone is used to collect MEMS sensors data at the same time [25]. After the time synchronization procedure, the fulfilled dataset is used to train the DMDD model. In order to reflect various situations in daily use, this paper recruited 10 volunteers to collect sensors data with different smartphone postures, including reading, phoning, pocket, and swaying, and the motion modes included stop, slow walking, normal walking, and fast walking, as well as other motion modes. The total movement distance of the training dataset is more than 120 km, and the total time is more than 38 h. In the training phase, some public data sets are mixed together for training purposes. The test dataset contains 20 trajectories with the ground-truth value for the positioning error evaluation, which is collected and modelled in the same way as in the training dataset.  
To evaluate the accuracy of the DMDD, a 2D test route including the indoor and outdoor areas is conducted, which is shown in Figure 4. Testers started at the point A, passed the point B, C, and D, and returned to point A, and repeated the same route three times in order to evaluate the long-term precision. The comparison of the DMDD-based trajectory estimation among complex motion modes is described in Figure 5.  
Figure 4 describes that the reading mode achieves the best localization performance, and the phoning mode achieves second-order accuracy, which is lower than the reading mode, the pocket mode achieves the third-order accuracy, and the swaying mode achieves the worst accuracy.  
Then, two state-of-art data-driven structures are adopted for comparison: RONIN [26] and IONNet [27]. The same test dataset is adopted for performance evaluation, which contains 20 trajectories with ground-truth value, and the comparison results are presented in Table 1.  
Table 1 indicates that the developed DMDD shows the best location estimation precision when comparing with the existing algorithms RONIN and IONet under complex motion modes. The reading mode achieves the highest accuracy of within 2.69 m, the phoning mode has the second-order accuracy of within 3.01 m, the pocket mode shows the third-order accuracy of within 3.74 m, and the swaying mode proves to have the worst positioning performance of within 4.47 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Reading** & **Phoning** & **Swaying** & **Pocket** \\ \hline DMDD & 2.69 m & 3.01 m & 4.47 m & 3.74 m \\ RONIN & 3.87 m & 4.12 m & 5.69 m & 4.28 m \\ IONet & 4.57 m & 4.84 m & 6.22 m & 4.99 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average errors of different handheld modes.  
Figure 4: Trajectories under different handheld modes.  
Figure 5: Performance comparison of Wi-Fi matching.