# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
###### Abstract  
The rise of mobile communication, low-power chips, and the Internet of Things has made smartwatches increasingly popular. Equipped with inertial measurement units (IMUs), these devices can recognize user activities through artificial intelligence (AI) analysis of sensor data. However, most existing AI-based activity recognition algorithms require significant computational power and storage, making them unsuitable for low-power devices like smartwatches. Additionally, discrepancies between training data and real-world data often hinder model generalization and performance. To address these challenges, we propose LIMUNet and its smaller variant LIMUNet-Tiny--lightweight neural networks designed for human activity recognition on smartwatches. LIMUNet utilizes depthwise separable convolutions and residual blocks to reduce computational complexity and parameter count. It also incorporates a dual attention mechanism specifically tailored to smartwatch sensor data, improving feature extraction without sacrificing efficiency. Experiments on the PAMAP2 and LIMU datasets show that the LIMUNet improves recognition accuracy by 2.9% over leading lightweight models while reducing parameters by 88.3% and computational load by 58.4%. Compared to other state-of-the-art models, LIMUNet achieves a 9.6% increase in accuracy, with a 60% reduction in parameters and a 57.8% reduction in computational cost. LIMUNet-Tiny further reduces parameters by 75% and computational load by 80%, making it even more suitable for resource-constrained devices. activity recognition; dual attention mechanism; depthwise separable convolution +
Footnote †: journal: Medicalalso reflect an individual's health status to a certain extent. By collecting and analyzing daily activity data, this technology can not only reveal the distribution of activities in a person's life but also provides suggestions for improving lifestyle habits based on these data. Such applications are of significant importance for promoting health management and enhancing quality of life. The main methods for human activity recognition at this stage include visual methods represented by video streams and images, wireless sensing methods represented by Wi-Fi signals and millimeter-wave radar, and wearable device collection methods represented by IMU (Inertial Measurement Unit) sensors. The data source chosen in this paper is the built-in IMU sensor of smartwatches, which belongs to the data collection method of wearable devices. Compared to the other two methods, using wearable devices can eliminate environmental constraints, as there is no need to deploy cameras in a specific environment in advance, nor is there a limitation to conducting activities in a specific setting; Moreover, when there is a lack of light in the environment, the data collection method of wearable devices can still function normally, while the effectiveness of visual methods will be significantly reduced. The cost of sensor devices is relatively low, and the data transmission volume is also small. In terms of privacy protection, the image information collected by visual methods contains a lot of information unrelated to human behavior, which poses a certain threat to user privacy. In addition, when wearable devices are combined with mobile phones, user data can only circulate and be processed on private devices without going through cloud servers, thus protecting user information security. In recent years, research based on data collected from wearable devices has gradually increased, but many studies rely on specific devices that have not yet encountered large-scale commercial scenarios. As a wearable device that has been validated by the market, smartwatches are favored by users of all age groups, making the use of smartwatches for human activity recognition a possibility for large-scale commercial applications.