期刊
IEEE SENSORS JOURNAL
卷 22, 期 6, 页码 6164-6174出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2022.3148431
关键词
Sensors; Feature extraction; Deep learning; Inertial sensors; Data mining; Convolutional neural networks; Accelerometers; Human activity recognition (HAR); inertial sensor; deep learning; Bi-GRU; inception architecture
资金
- Major Scientific and Technological Innovation Projects in Shandong Province [2019JZZY011111]
- National Natural Science Foundation of China [U21A20479]
This paper proposes a deep learning model based on inertial sensors for human activity recognition, which shows better performance and robustness compared to other methods. The impact of sensor configuration optimization is also analyzed.
Wearable sensor based Human Activity Recognition (HAR) has been widely used these years. This paper proposed a novel deep learning model for HAR using inertial sensors. First, a wearable device platform was developed with 6 inertial sensor units to collect triaxial acceleration signals during human movements, and the dataset of Command Actions of Traffic Police (CATP) was acquired. Then, a deep learning model named Bidirectional-Gated Recurrent Unit-Inception (Bi-GRU-I) was designed to improve the accuracy and reduce the amount of parameters. It is consisting of 2 Bi-GRU layers, 3 Inception layers, 1 Global Average Pooling (GAP) layer and 1 softmax layer. Finally, the comparing experiments with other methods were taken on 3 datasets: the self-collected CATP dataset, widely used Wireless Sensor Data Mining (WISDM) and University of California, Irvine (UCI-HAR) dataset. And the proposed method shows better performance and robustness. Moreover, the sensor configuration optimization was analyzed, and it shows that this method can also apply to the task using less sensor units.
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