# Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Subj.** & **Steps** & **Footwear** & **Feature** & **Classifier** & **Performance** \\ \hline [PERSON] et al. 2004 [17] & 11 & 440 & Barefoot & \begin{tabular}{c} Foot shape + \\ COP trajectory \\ \end{tabular} & HMM & \begin{tabular}{c} FRR: 1.36\%FAR: \\ 0.14\% \\ \end{tabular} \\ \hline [PERSON] et al. & 2007 [18] & 11 & 440 & Shod & \begin{tabular}{c} Vertical GRF \\ profile \\ \end{tabular} & \begin{tabular}{c} SVM, \\ MLP \\ \end{tabular} & ACC: 95\% \\ \hline [PERSON] et al. 2008 [15] & 40 & 2800 & Shod & 3D GRF profile & SVM & ACC: 98.2\% \\ \hline [PERSON] et al. & 2011 [16] & 104 & 1040 & Barefoot & \
\begin{tabular}{c} Plantar pressure \\ pattern \\ \end{tabular} & KNN & ACC: 99.6\% \\ \hline [PERSON] 2013 [14] & 142 & 2500 & Shod & 3D GRF profile & KNN & ACC: 98\% \\ \hline [PERSON] 2015 [23] & 92 & 3000 &
\begin{tabular}{c} Barefoot and \\ Shod \\ \end{tabular} & Mixed & KNN & ACC: 99.8\% (Barefoot) \\ \hline This study, identification & 36 & 108,000 & Shod & COP trajectory & CNN & ACC: 99.9\% \\ \hline This study, verification & 36 & 108,000 & Shod & COP trajectory & CNN & EER: 0.3\% \\ \hline \hline \end{tabular}  
* ACC: accuracy (correct classification rate). CNN: convolutional neural network. COP: center of pressure. EER: equal error rate. FAR: false acceptance rate. FRR: false rejection rate. GRF: ground reaction force. HMM: hidden Markov model. KNN: k-nearest neighbors. MLP: multilayer perceptron. SVM: support vector machine.  
\end{table} 
Table 4: Summary of representative studies in footstep recognition. with the results obtained for image classification [43]. The hypothesis that the use of depthwise separable convolution could favor a simpler and more computationally efficient model was not verified. Indeed, although slightly more accurate, the best sepCNN had over 2 M parameters versus 700 k for the best standard CNN. SepCNN appears to require far more filters per layer than CNN, which outweighs the fact that separable convolution requires fewer parameters. However, further analyses are needed to improve model architecture and select appropriate hyperparameters better adapted for sepCNN. Traditionally, CNNs used for image classification, such as AlexNet [55] or VGG [56], consist of two distinct parts--a feature extraction part (convolutional layers) and a classifier (fully connected, or dense, layers). In transfer learning, the feature extractor is frozen, and the dense classifier is replaced and trained for the new task. Modern CNN implementations, such as GoogLeNet [57], take advantage of fully convolutional networks, in which a global average pooling layer summarizes the feature maps before the output layer. This approach can also be applied to time series classification [22]. Hyperparameter tuning results (Table 1) highlighted that the second solution was better for the intended task of gait classification. The t-sne analysis (Figure 4) confirmed that the last two convolutional layers could classify new gaits on their own, without the help of dense layers. This is a major advantage in terms of computational efficiency, because convolutional layers require far fewer parameters than fully connected ones. By using distinct sets for model design and model testing, the risk of overfitting was minimized. Therefore, the high accuracy obtained in classifying gaits was most likely due to the actual similarity in distribution between the training and test sets. This highlights the constancy of COP trajectories over a 30-min walking session. COP patterns seem to exhibit a very low intra-individual variability as compared to inter-individual differences, at least in a time scale of half an hour. An extended time constancy of gait patterns is supported by several studies [17; 58; 59; 60; 61]. In 2018, [PERSON] et al. [59] showed that foot rotation and step width had intraclass correlation coefficients (ICC) greater than 0.93 when two measurements on different days were compared.