# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 2 Related Works  
HAR has been researched in many aspects, Despite the different approaches, be it vision-based, sensor-based, or Wi-Fi-based, HAR still faces substantial computational complexity issues. Edge computing, as a computing model that has gradually emerged in recent years, can transmit data to edge nodes for computation, reducing the computational burden on local devices. An early model of edge computing can be considered as Content Delivery Network (CDN) [11], which uses caching grid technology to deploy caching servers at different locations and distribute user access requests to the server closest to the user's location through the scheduling and load-balancing capabilities of the central cloud platform. This technology reduces network congestion and improves cache hit rate and response speed. [PERSON] proposed Cloudlet [12], which was deployed on a host at the edge of the network with more available computing resources. The uplink of Cloudlet is connected to the Internet, and the downlink is connected to mobile devices. Cloudlet provides edge services to reduce bandwidth and latency. Many scholars have conducted research on task offloading problems, which primarily concentrate on three aspects: (i) optimizing the energy consumption of mobile devices [13]; (ii) optimizing the communication and computation overhead between devices and clouds [14]; and (iii) optimizing the task completion time [15; 16]. The optimization of these aspects can effectively improve the performance of edge computing in human activity recognition tasks, making it more efficient and accurate. Wearable sensors are local devices in edge computing environments, usually installed on various parts of the body, such as the wrist and ankle. They obtain human behavioral data by measuring parameters such as acceleration, angular velocity, and direction. This data is then input into deep learning models for training and analysis, enabling automatic recognition of human activity [17]. Modern devices, such as smartphones and smartwatches, have integrated a range of sensors for detecting human behavior, including accelerometers, gyroscopes, and magnetometers. Researchers typically use data collected from these sensors and apply deep learning models to predict corresponding human actions. [PERSON] proposed a deep CNN approach for human activity recognition by learning features between multiple sensors and analyzing the temporal relationships between sensor data [18]. [PERSON] designed a motion recorder that uses accelerometers and gyroscopes to record common limb movement data and identifies human behavior actions using three supervised learning algorithms [19]. Deep learning networks have shown excellent performance in processing sensor data to predict human activity. Over the last few years, several deep learning architectures have been proposed, including RNN [20], CNN [21], and a combination of the two (CNN-RNN) [22]. The long short-term memory recurrent neural network (LSTM) method [23], combined with CNN, has demonstrated exceptional performance in analyzing sensor signal data and has become the mainstream for human activity recognition methods [24]. [PERSON], [PERSON] et al. built a multi-stage deep learning network framework [25], which pre-processes and features data gathered from wearable sensors using variational autoencoders (VAE). Later, they utilized deep convolutional generative adversarial networks (GAN) to process data from VAE processes. Their study achieved an average accuracy of 88.4% on HAR, outperforming other RNN models. [PERSON], [PERSON] et al. proposed an LSTM-based evidence generation/combination model [26] that employs several parallel networks instead of one large network as well as a new evidence-theoretic algorithm to enhance the recognition ability of the model. Their approach is considered in terms of time consumption and scale. It achieved higher recognition accuracy than the traditional method on the UCI-HAR Dataset.