# Machine Learning-Based Smartphone Grip Posture Image Recognition and Classification
## 5 Discussion
In contrast, MobileNetV2 and ResNet-based models, with their lightweight or residual architectures, appear to generalize more efficiently on small, shape-oriented datasets such as grip posture images. [PERSON] et al. [65] explored suitable combinations of models and optimizers for facial recognition by testing MobileNetV2, Inception V3, and ResNet50 in combination with Adam, SGD, and RMSprop. Their results indicated that Inception V3 consistently yielded lower performance across all optimizers. The classification model developed in this study may be utilized to identify the user's smartphone grip posture and may serve as reference for designing ergonomic smartphone interfaces. The grip posture classification model developed in the present study may be utilized to identify the frequency of grip posture usage, which can then be referenced for designing optimal interface positions. For example, [PERSON] et al. (2020) used frequency of grip posture usage information to determine the optimal UI positions, ensuring that preferred UI locations in frequently used grip postures were given higher importance compared to those in less frequently used postures [1]. The classification model used in this study may be applied to smartphones as well as to products operated with one hand to identify and classify grip postures. For instance, this model may identify and classify grip postures when using household appliances, allowing for the identification of frequently used grip postures to be referenced for interface design. By using the model developed in this study to identify users' grip postures, it can contribute to designing product interfaces with high usability [66]. Lastly, the grip posture identification experiments in this study were performed in a laboratory environment using mock-ups, and the background of the image data needs to be evaluated in various environments. Although the smartphone grip posture image data in the present study were collected using mock-ups based on actual smartphone specifications, it is necessary to evaluate the model using commercially available smartphones to reflect real-world usage scenarios. Additionally, grip posture image data collected in this study were controlled with a black background. To ensure the model's applicability in real-world situations, it needs to be evaluated using image data detected in various backgrounds [67; 68]. Also, the unequal amounts of data for the seven grip postures are thought to have affected the model's accuracy. Ensuring balanced data collection for each posture in future studies may improve the model's accuracy. To address the issue of data imbalance, techniques such as applying class weighting during training, oversampling minority classes, or using synthetic data augmentation may be considered. Although the model achieved relatively high classification accuracy, the number of training epochs used for some models (8-15) was somewhat limited. These values were selected based on preliminary experiments to avoid overfitting, but future work may involve more systematic hyperparameter tuning--including adjustments to the number of epochs--to further improve model performance and generalizability. Future studies may aim to develop grip posture classification models that better reflect real-world usage environments, expand the range of recognizable grip postures, and apply the findings to UI/UX optimization in home appliances. Improving the model's preprocessing methods and training it with images captured in diverse backgrounds may enable robust grip posture classification even in visually complex environments. This approach is expected to enhance the model's practical applicability in real-world usage scenarios. In addition, collecting data under various lighting conditions and from individuals with diverse skin tones may help improve classification accuracy in real-world environments.