## 4 Materials and Methods
### Methods
**Stride:** The stride parameter specifies the step size at which the kernel moves across the input data during convolution. A larger stride reduces the spatial dimensions of the output feature maps. **Padding:** Padding is used to preserve the spatial dimensions of the input data when applying convolutional filters. It involves adding zeros around the input data to ensure that the output feature maps have the desired size. **Batch Size:** The batch size determines the number of samples processed by the model in each training iteration. Larger batch sizes can accelerate training but may require more memory. **Activation Function:** Activation functions introduce nonlinearity into the network and enable it to learn complex mappings between inputs and outputs. Common activation functions include ReLU, sigmoid, and tanh. **Dropout:** Dropout is a regularization technique used to prevent overfitting by randomly deactivating a fraction of the neurons during training. **Learning Rate:** The learning rate controls the step size of the gradient descent algorithm used to update the model weights during training. It influences the speed and stability of the training process. Through meticulous tuning of these hyperparameters, researchers and practitioners can optimize the performance of CNNs for specific tasks and datasets, thereby enhancing generalization and predictive accuracy. Various techniques, such as grid search, random search, and Bayesian optimization, can be employed to identify the optimal combination of hyperparameters for a given problem. The overall CNN architecture for the current study is illustrated in Figure 2. Labeled acceleration data sampled at regular intervals were inputted into 1D convolutional layers. Interspersed between these layers were dropout layers, which served to mitigate overfitting of the neural network structure to the training data. Additionally, batch normalization layers were incorporated to normalize activations in the intermediate layers, thereby improving the accuracy. In the upcoming section, we will detail a comprehensive strategy for hyperparameter optimization, a critical phase aimed at refining the effectiveness of our network architecture. Through extensive experimentation and systematic adjustment of the hyperparameters, our objective was to identify the optimal configuration that maximized the network's performance across various metrics. This rigorous process ensured the robustness and adaptability of our model, allowing us to gain valuable insights into the complex interactions among different architectural components and their influence on the network's predictive abilities. The advent of deep learning introduced complex architectural structures with multiple layers, each governed by a set of hyperparameters determined by the designer. While some hyperparameters, such as optimization algorithms and activation functions, involve straightforward selection from a limited pool of options, others, including the number of layers and neurons, learning rates, and kernel sizes, require meticulous consideration due to their broad range of potential values. Choosing the appropriate hyperparameter values is often an iterative process, as the initial selections may not yield optimal results. Designers typically adjust these parameters iteratively, observing the model's performance with each change to identify the most suitable hyperparameter combination. Additionally, automated methods exist to streamline this selection process. Two common hyperparameter tuning techniques are random search and grid search. In random search, values are randomly selected from predetermined ranges for each hyperparameter, with iterations continuing until the best-performing combination is found. On the other hand, grid search evaluates all possible combinations within specified ranges to identify the optimal hyperparameter group. The concept of random search for hyperparameter optimization was initially  
Figure 2: Proposed neural network in present study. proposed by [PERSON] et al. [35].