# Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks
## 5 Conclusions  
In this work, a novel Deep Learning system for detection of abnormal foot postures and functions has been presented. The system is based on an instrumented footwear insole, capable of detecting the force exerted on different points of the insole (thanks to a low-power embedded system with FSR sensors) and sending the information to a computer via bluetooth. Moreover, an user application has been developed in order to do a real-time monitoring of these sensors' values, create a dataset and detect these abnormal foot postures. The novelty of this work corresponds to the neural network integrated into the user application in order to successfully detect the different footprint types. This neural network can be trained and tested using the same user application indicated above (the user can modify some features of this network in order to adapt it); and the trained system run real-time classifications during the user march. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Work** & **Year** & **Device** & **Method** & **Classification** & **Accuracy** & **Other metrics** \\ \hline
[28] & 2017 &
\begin{tabular}{c} Commercial \\ Insole (IMU) \\ \end{tabular} & \(\hat{z}^{2}\) & Gait type [4 ] & 92\% & X \\ \hline
[29] & 2016 & Shoe (IMU) & DTA [1 ] & Gait type & 97\% & X \\ \hline
[30] & 2019 & Insole (5 xFSRs) & Custom & Steps [5 ] & 98.5\% & X \\ \hline
[31] & 2015 & Insole (4 xFSRs) & LDA [2 ] and DTA & Gait type & 98.7\% & X \\ \hline
[32] & 2017 & Shoe (Accel.) & NN [3 ] & Gait type & 93\% & X \\ \hline
[33] & 2019 & \
\begin{tabular}{c} Shoe (Accel. and \\ 3x Piezoresistive) \\ \end{tabular} & NN & Gait info [6 ] & X &
\begin{tabular}{c} Errors: \\ \(-5.9\pm 37.1\) ms \\ \(11.4\pm 47.4\) ms \\ \end{tabular} \\ \hline
[34] & 2016 & Shoe (IMU) & NN & Gait info & X &
\begin{tabular}{c} Error: 10\% \\ \(\pm\) 0.12 s \\ \end{tabular} \\ \hline
[35] & 2018 &
\begin{tabular}{c} Smartphone \\ (Accelerometer) \\ \end{tabular} & NN & Gait type & 91.8\% & X \\ \hline
[20] & 2019 & Insole (IMU) & NN & Gait type & 90\% & X \\ \hline
**This** & 2019 & Insole (7 xFSRs) & NN & Foot posture [7 ] & 99.4\% &
\begin{tabular}{c} Sensitivity: 99.14\% \\ Specificity: 99.56\% \\ F1-Score: 99.12\% \\ \end{tabular} \\ \hline \hline \end{tabular}  
* _DecisionTreeAnalysis; \({}^{2}\) LinearDiscriminantAnalysis; \({}^{3}\) NeuralNetwork; \({}^{4}\) Walk,Run,Sprint,Sit,...; \({}^{5}\) Countrumberofsteps;\({}^{6}\) Stepslenght,width,changes,time,footangle,...; \({}^{7}\) Prorator,supinator,neutral..._  
\end{table} 
Table 7: Accuracy comparison with related works. The neural network classification has been tested by two methods: on one hand, a static test with a testing dataset (30% of the full dataset) is done on the system; and, on the other hand, a real-time execution test is evaluated. In the first test scenario, the classification results provide a F1-score above 98% (with an average accuracy above 99%); and, in the second scenario, the classification results provide a F1-score around 90% (with an average accuracy above 95%). In both cases the worst results are given by the class \"neutral\" with a 98.47% and a 88.75% values detected from the total samples, respectively. The results obtained demonstrate that the system is able to properly detect the three footprints types with a global error less than a 0.86%. It must, however, be noted that this work can only be considered a preliminary study. The datasets are obtained from a small set of voluntary users, so a bigger sample would be needed to obtain statistically relevant results. Conceptualization: M.J.D.-M. and [PERSON]; methodology: M.J.D.-M. and [PERSON]-[PERSON]; software: M.J.D.-M.; validation: [PERSON] and [PERSON]; formal analysis: [PERSON] and [PERSON]; investigation: [PERSON] and [PERSON]; writing: [PERSON], [PERSON] and [PERSON]; supervision: [PERSON] and [PERSON]. This research was funded by the Telefonica Chair \"[PERSON] Networks\" of the Universidad de Sevilla, Spain.