TY - GEN
T1 - A Novel Human Activity Recognition Model
AU - Zeng, Xinyi
AU - Huang, Menghua
AU - Zhang, Haiyang
AU - Ji, Zhanlin
AU - Ganchev, Ivan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the continuous improvement of the living standard, people have changed their concept of disease treatment to health management. However, most of the current health management software makes recommendations based on users' static information, with a low frequency of updates. The effect of targeted suggestions becomes weak with the passage of time, and it is hard for their recommendation effect to be satisfactory. Based on the use of smartphones for recognizing human activities on a real-time basis, a novel 'CNN+GRU' model is proposed in this paper†, utilizing both convolutional neural networks (CNNs) and gated recurrent units (GRUs). 'CNN+GRU' is able to extract the features in sensor data more accurately and improve the recognition speed. The proposed model was evaluated on a public data set, where it achieved an average accuracy of 91.27%, thus outperforming other models participating in the performance comparison experiments. In summary, the 'CNN+GRU' model can effectively recognize mobile users' activities based on the sensor data collected by their smartphones.
AB - With the continuous improvement of the living standard, people have changed their concept of disease treatment to health management. However, most of the current health management software makes recommendations based on users' static information, with a low frequency of updates. The effect of targeted suggestions becomes weak with the passage of time, and it is hard for their recommendation effect to be satisfactory. Based on the use of smartphones for recognizing human activities on a real-time basis, a novel 'CNN+GRU' model is proposed in this paper†, utilizing both convolutional neural networks (CNNs) and gated recurrent units (GRUs). 'CNN+GRU' is able to extract the features in sensor data more accurately and improve the recognition speed. The proposed model was evaluated on a public data set, where it achieved an average accuracy of 91.27%, thus outperforming other models participating in the performance comparison experiments. In summary, the 'CNN+GRU' model can effectively recognize mobile users' activities based on the sensor data collected by their smartphones.
KW - convolutional neural network (CNN)
KW - feature extraction
KW - gated recurrent unit (GRU)
KW - health management
KW - human activity recognition (HAR)
KW - UCI data set
UR - http://www.scopus.com/inward/record.url?scp=85190607844&partnerID=8YFLogxK
U2 - 10.1109/MCSI60294.2023.00024
DO - 10.1109/MCSI60294.2023.00024
M3 - Conference Proceeding
AN - SCOPUS:85190607844
T3 - Proceedings - 2023 8th International Conference on Mathematics and Computers in Sciences and Industry, MCSI 2023
SP - 101
EP - 106
BT - Proceedings - 2023 8th International Conference on Mathematics and Computers in Sciences and Industry, MCSI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Mathematics and Computers in Sciences and Industry, MCSI 2023
Y2 - 14 October 2023 through 16 October 2023
ER -
