# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
## 2 Materials and Methods
### Neural Network Architecture  
A deep learning model is proposed for the classification of unseen non-target movements, as illustrated in Figure 3. The proposed model is based on a combination of convolutional neural networks (CNN) and bidirectional long short-term memory (Bi-LSTM) layers, which are well-suited for analyzing time-series data. The network architecture comprises four convolutional blocks, where each block consists of three one-dimensional (1D) convolutional layers followed by a max-pooling layer. The convolutional layers in each block have a kernel size of 3, with the number of filters progressively increasing to 8, 16, 32, and 64 across the four blocks. Each convolutional block is followed by a max-pooling  
Figure 2: Data communication flow. Sensor signals are transmitted to a mobile phone via Bluetooth and recorded on a personal computer connected by USB. layer that applies a pooling size of 3 with a stride of 2, which progressively reduces the dimensionality of the feature maps. After the final convolutional block, the model incorporates two bidirectional LSTM layers with 64 and 32 units, respectively, to effectively capture temporal dependencies in the input sequences. Subsequently, two dropout layers are applied with a dropout ratio of 0.5 to prevent overfitting during training. The final classification is performed through two fully connected layers. The first layer contains 200 units and the output layer consists of 6 units, corresponding to the classification of target and non-target movements. The filter sizes, number of filters, and dropout ratios were empirically determined through extensive experimental evaluations to optimize the model's performance. The choice of this architecture was guided by the need to balance classification performance and computational efficiency, particularly given the size and characteristics of the dataset. The CNN module captures local spatial features of the movement signals, while the Bi-LSTM layers learn temporal dependencies by utilizing both past and future signals. This combination enhances the model's ability to distinguish between target and non-target movements. A combination of CNN and Bi-LSTM was chosen over more recent architectures such as Transformer or GRU because it provides a good trade-off between model complexity and accuracy for relatively small-scale gesture data. In preliminary trials, the proposed architecture showed stable training and competitive accuracy, making it suitable for the problem setting in this study. The proposed model utilizes a four-layer 1D CNN with increasing filter sizes (8, 16, 32, 64) to extract spatial features, followed by max-pooling (3, 2) and dropout (0.5) to reduce overfitting. These architectural choices were based on prior studies in time-series classification and confirmed through preliminary experiments. A kernel size of 3 was selected to capture fine-grained local features, while the increasing number of filters across layers allows the model to learn progressively higher-level representations. The dropout rate of 0.5 is a widely used heuristic that provides a good balance between regularization and model capacity. Bidirectional LSTM layers (64, 32 units) capture temporal dependencies in both directions, and fully connected layers (200, 6) perform final classification, maintaining a balance between efficiency and generalization. Figure 3: Network architecture for gesture classification. Blue arrows show data flow; blue blocks are convolutional layers, and gray blocks are dropout layers. A loss function was designed to improve classification performance by considering both class identification errors and the distinction between target and non-target patterns. The total loss function consists of two components: the categorical cross-entropy loss for class identification and an additional loss term that enhances the separation between target and non-target classes.