# Machine Learning-Based Smartphone Grip Posture Image Recognition and Classification
## 3 Methods
### Model Architectures  
To train and classify grip posture images in the present study, three models were used: MobileNetV2, ResNet-50, and Inception V3. Three pre-trained models were fine-tuned for the classification task. In all the three models, the layers of the base models pre-trained on ImageNet were frozen. Only the newly added classification layers, which included a GlobalAveragePooling2D layer, a fully connected Dense layer with 1024 units, a Dropout layer, and a final Dense layer for 7-class classification, were trained on the target dataset. MobileNetV2 is an optimized, lightweight deep learning model with an architecture that maximizes computational efficiency and minimizes memory usage. Improved from the architecture of MobileNetV1, it introduces a new feature: the inverted residual with linear bottleneck, utilizing high-efficiency depthwise separable convolutions and ReLU6 activation functions (Figure 7) [27; 28; 52; 53]. MobileNetV2 has advantages in adaptability to different environments and requirements, providing high accuracy and a lightweight framework for image classification [53], making it suitable for classifying grip posture images.  
Next, ResNet-50 is a convolutional neural network with 50 layers that learns residuals instead of directly learning features [31; 54]. ResNet-50 addresses the gradient vanishing problem, enabling the construction of deep neural networks [55]. As shown in Figure 8, the stacked layers produce an output defined as \(\mathrm{y=F(x)+x}\), where \(\mathrm{F(x)}\) represents the layer's output, and the initial input \(\mathrm{x}\) is added element-wise [56]. This model is particularly effective for image classification tasks, offering benefits such as easy optimization and low computational burden [57].  
Figure 6: The same grip posture type (L4-R1) for the left hand and the right hand.  
Figure 7: MobileNetV2 architecture [27].  
Lastly, Inception V3 is a CNN-based model comprising both symmetric and asymmetric components [35; 58]. These components include convolutional layers, max pooling, average pooling, dropouts, concatenations, and fully connected layers, as shown in Figure 9. By extracting features at multiple levels, Inception V3 enhances computational efficiency and requires less computational power [59].  
Furthermore, an ensemble of MobileNetV2, ResNet-50, and Inception V3 is used in this study. Ensemble learning leverages various machine learning algorithms, combining their strengths and compensating for their weaknesses, resulting in superior performance compared to a single algorithm [60]. This study employs the soft voting technique, which integrates the probabilistic outputs of individual models to generate a final prediction. By averaging the predicted probabilities of each algorithm, soft voting effectively balances their contributions, further enhancing the robustness and accuracy of the ensemble model [39]. As shown in Figure 10, ensemble learning integrates multiple machine learning algorithms into a unified framework, leading to improved prediction accuracy [39].  
Figure 8: ResNet-50 architecture [31].  
Figure 10: Ensemble model including MobileNetV2, ResNet-50, and Inception V3.  
Figure 9: Inception V3 architecture [35; 59].