# Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure
## 4 Discussion
According to [PERSON] et al., deep learning was used to determine the flatfoot in left and right feet using 413 right foot samples and 422 left foot samples; this study achieved over 80.00% for the classification accuracy in left and right feet (Table 2). [PERSON] et al. and [PERSON] et al. showed that left foot detection is higher than that of right feet [41; 42]. Meanwhile, [PERSON] et al. and our results showed accuracy of the left foot was lower than for right feet [43]. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**References** & **Data Acquisition Tools** & **Model** & **Dataset** & **Model Performance** & **Right** \\ \hline [PERSON] et al. (2020) [41] & Electroencephalogram & CNN & 64 EEG channels & 79.90\% & 78.60\% \\ [PERSON] et al. (2019) [42] & Smart inside & TGINN & 835 images & 82.35\% & 81.93\% \\ [PERSON] et al. (2020) [43] & Video frames & ANN & 2391 images & 94.00\% & 95.00\% \\ Our Study & Plantar pressure & YOLO & 974 images & 93.35\% & 95.93\% \\ \hline \hline \end{tabular} Note: ANN, Artificial Neural Networks; CNN, Convolutional Neural Network; EEG, Electroencephalogram; TGINN, Triple Generalized-Inverse Neural Network; YOLO, You Only Look Once; our study value was average from YOLO series.  
\end{table} 
Table 2: The comparison of studies in left and right foot prediction using deep learning approach. According to the [PERSON] et al. study, the dominant right leg showed higher accuracy (80%) than the dominant left leg (20%) [44]. In our results, the left and right feet have different results of \(AP\) due to the side difference between the dominant and non-dominant legs. The dominant leg may induce higher accuracy in the right foot. The dominant leg contributes more significantly to producing the plantar pressure distribution image pattern toward forwarding propulsion, while the non-dominant leg contributes toward support [45]. It is concluded that the dominant leg bearing the main body weight may affect the pressure and lead to loss of the foot features in plantar pressure images on the non-dominant leg (left foot). This may be affected by the short walking time that requires a more dominant leg (right foot). It produces more pressure on the footprint images, for determining the left and right foot results is more accurate than long-time walking [46]. Loss of foot features in plantar pressure images may affect the left foot YOLO series detection. We compared our results with other studies that used deep learning to detect the left and right feet. Our study used plantar pressure images to identify the left and right feet. Furthermore, object detection with bounding box annotation was used to localize and classify the left and right feet. On the other hand, several models are used to identify left and right feet in deep learning. [PERSON] et al. (2020) used the artificial neural network (ANN) model to classify the human body part's movement, including determining the left and right feet [43]. [PERSON] et al. (2018), used a convolutional neural network (CNN) classify foot type using heterogeneous pressure data; many variations in human pose and image quality introduced complications [41]. [PERSON] et al. (2019) used a triple generalized-inverse neural network (TGINN) to classify the left and right feet under a flat foot using a smart insole [42] (Table 2). However, the above three studies did not use the object detection method and showed around a 0%-16% range from object detection. Therefore, the main reason for our research was to select the object detection method that can localize and classify the image features of plantar pressure images. As a result, it can achieve a higher accuracy (over 93%) with small datasets. Furthermore, object detection may be suitable for determining where objects are located in a given image and which category each object belongs to [47]. We used the smallest dataset with the multi-color channel in this study. The data acquisition tools may affect the accuracy of left and right foot detection.