Prediction Analysis of Greeting Gestures Based on Recurrent Neural Networks

Human activity recognition, such as rehabilitation, sports, human behavior, etc., is developing rapidly. A Recurrent Neural Network (RNN) is a practical approach to human activity recognition research and sequential data. However, studies on recognizing human activities rarely study culture, including greeting gestures. And studies seldom use small datasets when employing the RNN approach, as they typically utilize large amounts of data to conduct such studies. This study aims to predict greeting gestures from Japan and Indonesia with limited data. This study proposes and compares six RNN architecture methods, including Long Short-Term Memory (LSTM), Bidirectional RNN (BRNN), Gated Recurrent Unit (GRU), Vanilla RNN (VRNN), Deep RNN (DRNN), and Hierarchical RNN (HRNN), which have been modified with regularization to handle overfitting. We evaluate using Mean Squared Error (MSE), Root Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). The experimental results show that LSTM has the best MSE, RMSE, and MAE values, with MSE of 0.0773479, RMSE of 0.2781149, and MAE of 0.2402451, while GRU has the best R² value of 0.0267571. The conclusion of this study indicates that LSTM and GRU are more suitable than other models for solving this problem. Therefore, it can be beneficial for future research to address the challenges of small data and overfitting in sequential data and human activity recognition, particularly in the context of greeting gestures. Future work can utilize data augmentation, proper parameter selection, and incorporate data from multiple individuals to enhance the accuracy of the model.