# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 3 Limitations of Previous Works and Contributions  
Table 1 outlines the issues with existing works, which often exhibit one or more of the following limitations:  
* The system outlined in [16] is intricate, despite its high accuracy, as it involves processing double instances of each dataset. * The model presented in [21] boasts good accuracy but offers fewer classes and limited datasets from various sources. * In contrast to the work described in [24], which achieves high accuracy but lacks diversified datasets, the approach addressed in [19] only achieves 81%, a comparatively lower result. * Whereas the model mentioned in [17] provides good accuracy, its applicability is restricted to specific age groups. Moreover, it is gender-specific and suitable only for slow speeds. * The model in [25] conducted experiments with fewer classes. Achieving a deep understanding of image properties is imperative when relying on a fixed set of handcrafted features [17, 18, 25]. These methods rely on texture analysis, where classifiers like RF are fed a limited number of locally computed descriptors from the image. Whereas some studies have demonstrated the high accuracy of these strategies, they are constrained in terms of generalizability and inter-dataset variability. Some of the aforementioned models have demonstrated exceptional accuracy in their respective work. However, they are often tested in a limited number of classes. Others may have a sufficient number of classes but are limited to specific age or gender groups and can only operate within certain speed limits. Additionally, some researchers have employed diverse datasets, but they often belong to the same modality. In light of the challenges outlined in Table 1, this study makes the following significant contributions:  
* Transformer-based single-modal models, MViTv2_base and MViTv2_small, were introduced using the original instances of the foot pressure dataset. These models exhibit enhanced performance in gait classification from foot pressure data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Method** & **Dataset** & **Accuracy (\%)** & **Limitations** \\ \hline [PERSON] et al. [16] & RNN and CNN & Foot pressure dataset & 97.60 & \begin{tabular}{c} Complicated \\ Uses double instances \\ of datasets \\ \end{tabular} \\ \hline [PERSON] et al. [21] & CNN & \begin{tabular}{c} Micro-Doppler and \\ Interferometric \\ Spectrogram Image \\ \end{tabular} & 99.5 & \begin{tabular}{c} Small number of classes \\ Lack of diverse datasets \\ \end{tabular} \\ \hline [PERSON] et al. [29] & CNN & Casia-B & 100 & \begin{tabular}{c} Overfitting \\ Lack of diverse datasets \\ \end{tabular} \\ \hline [PERSON] et al. [18] & \begin{tabular}{c} SVM, RF, Logistic \\ Regression \\ \end{tabular} & \begin{tabular}{c} Open dataset based on \\ inertial sensor-based \\ system \\ \end{tabular} & 81 & \begin{tabular}{c} Low accuracy \\ Lack of diverse datasets \\ Handcrafted features \\ \end{tabular} \\ \hline [PERSON] et al. [17] & XGBoost, ResNet & \begin{tabular}{c} Open dataset based on \\ inertial sensor-based \\ system \\ \end{tabular} & 93.75 & \begin{tabular}{c} Restricted age group \\ Gender discrimination \\ Restricted speed \\ Lack of diverse datasets \\ Handcrafted features \\ \end{tabular} \\ \hline [PERSON] et al. [25] & KNN, RF, DT, LR, SGD & EMG dataset & 99 & \
\begin{tabular}{c} Less number of classes \\ Handcrafted features \\ \end{tabular} \\ \hline [PERSON] et al. [24] & GaitRec-Net &
\begin{tabular}{c} GRF measurements from \\ different patients \\ \end{tabular} & 91.6 & Lack of diverse datasets \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison and shortcomings of earlier works. * A single-modal approach, the ST-GCN model, utilizing the original instances of the skeletal dataset, was presented. * To enhance the accuracy of the foot pressure dataset, various augmentations were introduced. * The proposed single-modal models outperform the baseline single-modal models.