# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
###### Abstract
Smartwatches, as a type of wearable device worn at a single location, have a lower data volume and greater noise compared to wearable devices worn at multiple locations, which poses high demands on activity recognition algorithms. At the same time, performing computations at the edge also encounters issues such as excessive algorithm complexity and data distribution discrepancies. Several years ago, traditional machine learning methods, such as decision trees, random forests, and support vector machines, were widely applied in the field of human activity recognition, effectively promoting research and development in this area. These methods provided new avenues for understanding complex behaviors by analyzing human activity patterns. However, they often rely on the expertise of domain experts and manual feature extraction, which not only has high technical requirements but is also costly and prone to human error, affecting recognition accuracy. These limitations are particularly evident when dealing with daily human-activity-recognition tasks, especially when faced with complex or irregular behavior data. In recent years, with the rapid development of technology, automatic learning [8; 9], feature selection [10; 11; 12] and feature extraction [13; 14] techniques have become research hotspots [15]. For example, new methods such as deep learning have reduced the reliance on specialized knowledge and improved the efficiency and accuracy of feature extraction. These advancements enable machine learning models to learn and recognize specific patterns more effectively from complex data, improving the performance of human activity recognition tasks while also reducing research and development costs and the error rate of recognition. Many achievements in human activity recognition have emerged based on deep learning methods, but most of these works are difficult to deploy in practical devices. At the current stage, most research on human activity recognition using smartwatches faces the challenge of lightweight deployment, as many works in activity recognition did not consider lightweight design when developing neural networks. Although large and deep neural networks generally possess stronger recognition capabilities, the design of such algorithms leads to a massive increase in parameters and computational load. Ingeneral, smartwatches require lightweight algorithms to ensure the real-time nature of the recognition process and the operability of the recognition system. To address this challenge, this study adopts lightweight depthwise separable convolution as the basic unit, combined with residual modules and dual attention mechanisms to enhance the model's feature extraction capabilities. Specifically, the contributions of this paper can be summarized in the following three points:  
1. Application of the dual attention mechanism: This paper innovatively combines the channel attention mechanism and the sensor attention mechanism, enabling the neural network to perform independent feature extraction and enhancement at both the channel and sensor levels. This dual attention mechanism not only enhances the model's ability to focus on important features but also improves the network's adaptability and generalization ability
2. Lightweight depthwise separable convolution structure: Depthwise separable convolution is employed to replace traditional convolution, significantly reducing the model's parameter count and computational complexity. At the same time, a residual bottleneck layer is introduced, which first expands the number of channels and then performs convolution processing, further enhancing the feature extraction capability and the lightweight nature of the model. This design is particularly suitable for deployment on resource-constrained devices such as smartwatches. 3. Design of user behavior recognition algorithms for smartwatches: The proposed LIMUNet and its simplified version LIMUNet-Tiny are specifically optimized for user behavior recognition scenarios on smartwatches.