# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 2 Related Works
### Lightweight Models  
In existing research, most deep CNN models are large-scale and computationally intensive. Due to high computational resource demands, many high-performing models have significant limitations in real-world scenarios, especially in the edge intelligence domain with widespread application needs, such as smartwatches and smartphones. Therefore, the lightweight model is crucial in the problem of human activity recognition (HAR) based on smartwatches, as edge devices are characterized by weak computational power and limited storage space. Especially in the field of deep learning, lightweight algorithms are designed to operate in resource-constrained environments by reducing the parameter count and computational complexity of deep neural networks, aiming to maintain high performance while minimizing storage space and computational cost, making them particularly suitable for mobile devices, embedded systems, and other scenarios where resources are limited. Current cutting-edge research on lightweight convolution primarily focuses on the visual domain, with the Inception series of GoogLeNet continuously making progress in improving accuracy and computational complexity [30; 31; 32]. In Inception v1 [30], the authors proposed a convolutional neural network called Inception, which combines the convolution results of different convolution kernels, allowing a single-layer convolutional neural network to produce results processed by convolution kernels of various sizes. This method enables the network to learn which type of convolution can yield optimal results. By employing pointwise convolution, Inception can reduce the number of channels, thereby decreasing the parameter count. In Inception v2 [31], in order to further reduce the parameter count and computational complexity of Inception, a convolution kernel is decomposed into one and two convolutions. Xception [32] uses depthwise separable convolution to reduce the redundancy of parameters in convolutional neural networks, thereby enhancing the lightweight nature of the Inception network in terms of parameter count and computational complexity. Squeezenet [25] achieves significant compression of both computational complexity and parameter count by using pointwise convolution to reduce the number of channels. ResNeXt [26] combines ResNet [33] with Inception [30] and employs grouped convolution to achieve lightweight modeling, significantly reducing the number of parameters. MobileNet [34] also applies depthwise separable convolution, which decomposes standard convolution into depthwise convolution and pointwise convolution, significantly reducing computational complexity. In order to reduce the computational complexity of pointwise convolution, ShuffleNet [35] introduced grouped convolution. In addition, MobileNetV 2 [36] proposed the residual bottleneck layer, which combines depthwise separable convolution with ResNet, effectively avoiding the gradient vanishing problem while enhancing the feature extraction capability of the neural network. Compared to visual systems, the development of lightweight design for human activity recognition systems is relatively slower. Currently, there are some studies on lightweight neural networks in the field of human activity recognition based on wearable devices. [PERSON] et al. [37] adopted the idea of grouped convolution, dividing the filters within specific convolution layers into two groups of different sizes. The two groups of filters complement each other, enhancing the reception range of the sensor signal while also reducing the parameter count and computational complexity. GTSNet [38] proposed the Grouped Temporal Shift (GTS) module, which reduces the number of network connections by grouping the channels of the signal and adds shifts in the temporal direction to enhance feature extraction capability, thereby achieving the lightweighting of the network. Existing lightweight networks in the field of activity recognition often adopt the method of grouped convolution.