.png){kind=icon}
返回
Recognition of human activity using GRU deep learning algorithm
.png){kind=icon}
OA
AI
DOI：10.1007/s11042-023-15571-y.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Human activity recognition (HAR) is a challenging issue in several fields, such as medical diagnosis. Recent advances in the accuracy of deep learning have contributed to solving the HAR issues. Thus, it is necessary to implement deep learning algorithms that have high performance and greater accuracy. In this paper, a gated recurrent unit (GRU) algorithm is proposed to classify human activities. This algorithm is applied to the Wireless Sensor Data Mining (WISDM) dataset gathered from many individuals with six classes of various activities - walking, sitting, downstairs, jogging, standing, and upstairs. The proposed algorithm is tested and trained via a hyper-parameter tuning method with TensorFlow framework to achieve high accuracy. Experiments are conducted to evaluate the performance of the GRU algorithm using receiver operating characteristic (ROC) curves and confusion matrices. The results demonstrate that the GRU algorithm provides high performance in the recognition of human activities. The GRU algorithm achieves a testing accuracy of 97.08%. The rate of testing loss for the GRU is 0.221, while the precision, sensitivity, and F1-score for the GRU are 97.11%, 97.09%, and 97.10%, respectively. Experimentally, the area under the ROC curves (AUC(S)) is 100%.
Keyword:
Deep learning
Gated recurrent unit (GRU)
Human activity recognition (HAR)
Artificial intelligence (AI)
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Deep Human Activity Recognition With Localisation of Wearable Sensors基于可穿戴传感器定位的深度人体活动识别.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challengesINFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏Deep Learning Approach for Intelligent Intrusion Detection System面向智能入侵检测系统的深度学习方法IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容