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篇名
Human Activity Recognition Based on CNN and LSTM
並列篇名
Human Activity Recognition Based on CNN and LSTM
作者 Xu-Nan Tan (Xu-Nan Tan)
英文摘要

Human activity recognition (HAR) based on wearable devices is an emerging field of great interest. HAR can provide additional information on a human subject’s physical status. Utilising new technologies for HAR will become very meaningful with the development of deep learning. This study aims to mine deep learning models for HAR prediction with the highest accuracy on the basis of time-series data collected by mobile wearable devices. To this end, convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) are combined in a deep network model to extract behavioural facts. The proposed CNN model contains two convolutional layers and a maximum pooling layer, and batch normalisation is added after each convolutional layer to improve convergence speed and avoid overfitting. This structure yields significant results in terms of performance. The model is evaluated on the MHEALTH dataset with a test set accuracy of 99.61% and can be used for the intelligent recognition of human activity. The results of this study show that the proposed model has better robustness and motion pattern detection capability compared to other models.

 

起訖頁 221-235
關鍵詞 human activity recognitionCNNLSTMdeep learningmodel integration
刊名 電腦學刊  
期數 202306 (34:3期)
該期刊-上一篇 A Machine Learning Based Approach to QoS Metrics Prediction in the Context of SDN
該期刊-下一篇 An End-to-End Multi-Scale Conditional Generative Adversarial Network for Image Deblurring
 

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