The Effect of Axis-Wise Triaxial Acceleration Data Fusion in CNN-Based Human Activity Recognition

Xinxin HAN  Jian YE  Jia LUO  Haiying ZHOU  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.4   pp.813-824
Publication Date: 2020/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7409
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
triaxial accelerometer,  human activity recognition,  data fusion,  convolutional neural network,  

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The triaxial accelerometer is one of the most important sensors for human activity recognition (HAR). It has been observed that the relations between the axes of a triaxial accelerometer plays a significant role in improving the accuracy of activity recognition. However, the existing research rarely focuses on these relations, but rather on the fusion of multiple sensors. In this paper, we propose a data fusion-based convolutional neural network (CNN) approach to effectively use the relations between the axes. We design a single-channel data fusion method and multichannel data fusion method in consideration of the diversified formats of sensor data. After obtaining the fused data, a CNN is used to extract the features and perform classification. The experiments show that the proposed approach has an advantage over the CNN in accuracy. Moreover, the single-channel model achieves an accuracy of 98.83% with the WISDM dataset, which is higher than that of state-of-the-art methods.