# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
###### Abstract  
Human Activity Recognition (HAR) has been proven to be effective in various healthcare and telemonitoring applications. Current HAR methods, especially deep learning, are extensively employed owing to their exceptional recognition capabilities. However, in pursuit of enhancing feature expression abilities, deep learning often introduces a trade-off by increasing Time complexity. Moreover, the intricate nature of human activity data poses a challenge as it can lead to a notable decrease in recognition accuracy when affected by additional noise. These aspects will significantly impair recognition performance. To advance this field further, we present a HAR method based on an edge-computing-assisted and GRU deep-learning network. We initially proposed a model for edge computing to optimize the energy consumption and processing time of wearable devices. This model transmits HAR data to edge-computable nodes, deploys analytical models on edge servers for remote training, and returns results to wearable devices for processing. Then, we introduced an initial convolution method to preprocess large amounts of training data more effectively. To this end, an attention mechanism was integrated into the network structure to enhance the analysis of confusing data and improve the accuracy of action classification. Our results demonstrated that the proposed approach achieved an average accuracy of 85.4% on the 200 difficult-to-identify HAR data, which outperforms the Recurrent Neural Network (RNN) method's accuracy of 77.1%. The experimental results showcase the efficacy of the proposed method and offer valuable insights for the future application of HAR. human activity recognition; edge computing; gate recurrent unit; wearable sensor +
Footnote †: journal: Applied  
such as bracelets. This approach can greatly enhance the user experience and prevent long-term damage to the devices caused by the high computational demands of traditional activity recognition methods. Additionally, the edge computing platform can provide real-time analysis and response, meeting the application delay limit requirements on wearable devices [7]. Overall, the use of edge computing in human activity recognition for wearable devices is a promising approach that can significantly improve the performance and usability of wearable devices in various applications. In addition, deep learning models for human activity recognition still face persistent challenges in time-awareness and training speed. The traditional recurrent neural networks (RNN) and convolutional neural networks (CNN) structures cannot effectively handle time-series data, leading to limitations in processing time-related human activity recognition tasks [8]. Moreover, the traditional CNN and RNN models require a large amount of computation when dealing with large-scale data, resulting in slow training speed. To address these issues, researchers have taken measures such as employing the convolutional recurrent neural network (CRNN) structure, which combines CNN and RNN to better handle time-series data and achieve higher accuracy. In terms of training speed, optimization methods like pretraining and mini-batch training have been adopted, significantly improving the training speed. Although these deep model optimization methods made some progress, there is still room for improvement when applied in HAR. In this paper, to address the challenges posed by slow training speed and low recognition accuracy in the presence of additional noise, we proposed an innovative approach that uses a new O-Inception Element-Wise-Attention GRU network (OI-EleAttG-GRU) model and integrates it into HAR analysis. In this model, the initial convolution method is used to better preprocess large amounts of training data for analysis [9]. The EleAttG-GRU component, as an improved version of the recurrent neural network, improves its ability to process temporal features by adding an attention mechanism to the network structure [10].