# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 2 Related Works
### Deep Learning Solutions for HAR  
Human activity recognition, as a means of human-computer interaction, holds special significance for some future intelligent applications. The introduction of deep learning methods has not only achieved strong results in fields such as speech recognition, image recognition, and object detection but has also demonstrated excellent recognition capabilities in the field of human activity recognition. In recent years, the introduction of convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) Networks, and attention mechanisms has provided human activity recognition tasks with more powerful feature extraction capabilities, thereby enabling algorithms to achieve better recognition performance. Based on the practical application scenarios of this study, the research on human activity recognition based on deep learning is divided into two categories: traditional models and lightweight models. [PERSON] et al. [18] proposed a feature extraction method based on convolutional neural networks for human activity recognition tasks, which can extract local features from sensor time series, resulting in better recognition performance than traditional handcrafted feature extraction methods. [PERSON] et al. [19] combined the time series data from accelerometers and gyroscopes into a behavior map and performed feature extraction using a two-dimensional convolutional neural network. [PERSON] et al. [20] introduced a binarized convolutional neural network, which can be used for real-time human activity recognition. In this network, dilated convolution is used to expand the receptive field and enhance its ability to capture time series features. This work can reduce the recognition latency in resource-constrained mobile devices, thereby expanding the application scenarios of activity recognition. In addition, numerous CNN architectures addressing the problem of human activity recognition have been proposed in recent years, achieving strong results in their respective fields [21; 22]. IMU data are essentially a type of time series data, so some neural network models that perform well on time series data are also applicable to the scenario of human activity recognition. [PERSON] et al. [23] proposed a novel deep model called AttnSense for multimodal human activity recognition tasks, which combines attention modules with convolutional neural networks and gated recurrent units (GRUs) to highlight more important sensor modalities. In addition, attention-based deep neural network models can enhance the interpretability of neural network model behaviors. [PERSON] et al. [24] proposed a multi-layer stacked LSTM network structure that can perform feature extraction on IMU time series data collected from smartphones, achieving an accuracy of 93.13% on the UCI dataset. [PERSON] et al. [25] introduced a deep recurrent neural network (DRCNN) based on LSTM, which implemented a bidirectional recurrent neural network for activity recognition and was capable of recognizing variable-length window data of human activities. [PERSON] et al. [26] designed a deep neural network based on RNN for the activity recognition scenario using a single IMU sensor located on the wrist, achieving results comparable to those in multi-sensor scenarios. With the development of deep learning technology, some researchers tend to adopt hybrid models that combine various types of network structures for activity recognition. [PERSON] et al. [27] chose to combine residual modules with bidirectional LSTM in a hybrid model, where the bidirectional LSTM module can effectively consider both past and future contextual information, while ResNet can avoid the gradient vanishing problem caused by overly deep networks. [PERSON] et al. [7] combined reinforcement learning with LSTM, enhancing the model's multimodal perception capabilities.