# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 2 Related Works
### Few-Shot Learning Model  
Current research shows that deep learning has achieved excellent results in tasks such as computer vision and natural language processing, but most of these studies rely on a large amount of training data. In practical application scenarios, especially in the case of using smartwatches for human activity recognition, it is clearly impractical to collect a large amount of data to train a new model when new users use the smartwatch or when new behavior patterns need to be recognized. This has led to the emergence of the research direction of few-shot learning.  
Data augmentation is a common few-shot learning method that expands the size of the dataset by enhancing limited data, thereby improving the generalization ability of deep learning algorithms. Methods of data augmentation are widely applied in visual methods, such as rotating images at different angles, adding noise to images, and changing image colors [39]. Similar methods can also be applied to time series data represented by audio data, IMU data, etc., specifically using techniques such as inversion, scaling, windowing, and adding noise for data augmentation. [PERSON] et al. [40] applied data augmentation to the problem of human activity recognition based on wearable devices and innovatively proposed a physics-based data augmentation method, which allows the transformations of data augmentation to be mapped to a specific physical state transformation, thereby improving the quality of the data obtained through data augmentation.  
In the field of machine learning, transfer learning is an effective technique for addressing small sample problems, particularly excelling in the application area of activity recognition. Specifically, transfer learning can fully utilize models that have been trained on source domains (such as large datasets) and adapt these models to new target domains with fewer samples through fine-tuning. This approach significantly reduces the dependence on large amounts of labeled data, accelerates the training efficiency of the model, and improves recognition accuracy. FSHAR [41] adopts a fine-tuning approach to transfer models trained on source domain data to the target domain, achieving small sample activity recognition. However, the pre-trained models of transfer learning are generally fitted to the source domain data, making it difficult to quickly transfer to the target domain in cases of very few samples in activity recognition.  
In recent years, methods of meta-learning have often been used to achieve few-shot learning [42; 43; 44; 45]. Meta-learning learns a general classification method from a majority of data and then performs few training iterations on tasks composed of a small number of samples to achieve usable classification results. Since meta-learning methods are not limited to a specific task during the learning phase, but are trained on randomly sampled tasks to obtain a general classification method, they exhibit better generalization capabilities. Existing research on meta-learning is generally divided into three categories: model-based methods, metric learning-based methods, and optimization-based methods. [PERSON] et al. [46] adopted the prototype network, a metric-based meta-learning approach, to achieve more effective few-shot activity recognition.