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作者:

Zhang, Cheng (Zhang, Cheng.) | Zu, Tianqi (Zu, Tianqi.) | Hou, Yibin (Hou, Yibin.) | He, Jian (He, Jian.) | Yang, Shengqi (Yang, Shengqi.) | Dong, Ruihai (Dong, Ruihai.)

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EI Scopus

摘要:

In recent years, human activity recognition (HAR) methods are developing rapidly. However, most existing methods base on single input data modality, and suffers from accuracy and robustness issues. In this paper, we present a novel multi-modal HAR architecture which fuses signals from both RGB visual data and Inertial Measurement Units (IMU) data. As for the RGB modality, the speed-weighted star RGB representation is proposed to aggregate the temporal information, and a convolutional network is employed to extract features; As for the IMU modality, Fast Fourier transform and multi-layer perceptron are employed to extract the dynamical features of IMU data. As for the feature fusion scheme, the global soft attention layer is designed to adjust the weights according to the concatenated features, and the L-softmax with soft voting is adopted to classify activities. The proposed method is evaluated on the UP-Fall dataset, the F1-scores are 0.92 and 1.00 for 11 classes classification task and fall/non-fall binary classification task respectively. © 2023, China Computer Federation (CCF).

关键词:

Wearable technology Fast Fourier transforms Convolution Pattern recognition Data mining Classification (of information) Fall detection

作者机构:

  • [ 1 ] [Zhang, Cheng]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Zu, Tianqi]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Hou, Yibin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Hou, Yibin]Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [He, Jian]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [He, Jian]Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing; 100124, China
  • [ 7 ] [Yang, Shengqi]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 8 ] [Yang, Shengqi]Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Dong, Ruihai]Insight Centre for Data Analytics, University College Dublin, Dublin 4, Ireland

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来源 :

CCF Transactions on Pervasive Computing and Interaction

ISSN: 2524-521X

年份: 2023

期: 3

卷: 5

页码: 321-332

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SCOPUS被引频次: 3

ESI高被引论文在榜: 0 展开所有

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