## 4 Conclusions and Prospects  
Studies have shown that the earlier rehabilitation training is, the better postoperative results will be. To make patients recover sooner from the hospital, a gait rehabilitation robot assisted training with some quantitatively detected sensor data is the most commonly used training method at present [22; 23], but it is mainly applicable to recover in the hospital without a precise and objective evaluation method of training effect [24; 25]. Therefore, combined with the'medical + internet' model, an app based on plantar pressure analysis is introduced, which can obtain the doctor's training plan via the internet  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Gender** & **Age** & **Disease** & **Rehabilitation Stage** \\ \hline P1 & Male & 81 & Femoral neck fracture & 3 months after surgery \\ P2 & Female & 69 & Lumbar spine disease & 2 weeks after surgery \\ P3 & Male & 84 & Femoral neck fracture & 3 weeks after surgery \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of subject  
Figure 12: The comparison of normal gait and patients’ gaits.  
and transmit it to the plantar pressure testing device. The training data can also be obtained from plantar pressure testing device and transferred to the doctor. In addition, patients and doctors can communicate in real time. Preliminary testing and pilot experiment showed that the app can obtain accurate and reliable plantar pressure for quantitative gait analysis. The characteristics of different stages in the same disease can be distinguished clearly. Thus, the rehabilitation laws can be summarized with the support of big data, providing decision basis for doctors to make follow-up training plans.  
At the same time, the design of the app provides a new way for patients to recover out of hospitals. For patients, some of the rehabilitation processes in the hospital can be transferred to the family with the help of the app, thus shortening hospital stays and saving costs. They can also learn about their recovery conditions in real time through the app, such as the training phase and training effect. For doctors, they can intuitively see the recovery of patients who are not in the hospital, and direct patient training in real time. In addition, the medical resources can be saved by conducting rehabilitation training for patients at home, making up for the current shortage of medical resources and the imbalance of distribution.  
On the other hand, telemedicine and apps are only technical means. To explore the general laws of postoperative rehabilitation by the app we developed is the most important, which is to provide guidance for various types of training and provide a basis for evaluating various rehabilitation techniques. In the future, a lot of experiments shall be done with our designed device and app to obtain more patients' pressure data of traditional recovery in different rehabilitation stages. The laws of postoperative rehabilitation will be worked out and be used as the evaluation method of various rehabilitation techniques with the help of big data and machine learning methods.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] contributed analysis tools and checked the paper. [PERSON] wrote the paper.  
This work has been supported by National Nature Science Foundation of China (no. U1713218, 61375090, U1613224), and National High-Tech R&D Program (863 Program) (no. 2015 AA043204).  
The authors declare no conflict of interest.