The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'accéléromètre triaxial est l'un des capteurs les plus importants pour la reconnaissance de l'activité humaine (HAR). Il a été observé que les relations entre les axes d'un accéléromètre triaxial jouent un rôle important dans l'amélioration de la précision de la reconnaissance d'activité. Cependant, les recherches existantes se concentrent rarement sur ces relations, mais plutôt sur la fusion de plusieurs capteurs. Dans cet article, nous proposons une approche de réseau neuronal convolutif (CNN) basée sur la fusion de données pour utiliser efficacement les relations entre les axes. Nous concevons une méthode de fusion de données monocanal et une méthode de fusion de données multicanaux en tenant compte des formats diversifiés de données de capteurs. Après avoir obtenu les données fusionnées, un CNN est utilisé pour extraire les caractéristiques et effectuer une classification. Les expériences montrent que l’approche proposée présente un avantage en termes de précision par rapport à CNN. De plus, le modèle monocanal atteint une précision de 98.83 % avec l’ensemble de données WISDM, ce qui est supérieur à celui des méthodes de pointe.
Xinxin HAN
North University of China,Chinese Academy of Sciences
Jian YE
Chinese Academy of Sciences,Beijing Key Laboratory of Mobile Computing and Pervasive Device
Jia LUO
Beijing University of Technology
Haiying ZHOU
North University of China
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Xinxin HAN, Jian YE, Jia LUO, Haiying ZHOU, "The Effect of Axis-Wise Triaxial Acceleration Data Fusion in CNN-Based Human Activity Recognition" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 4, pp. 813-824, April 2020, doi: 10.1587/transinf.2018EDP7409.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7409/_p
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@ARTICLE{e103-d_4_813,
author={Xinxin HAN, Jian YE, Jia LUO, Haiying ZHOU, },
journal={IEICE TRANSACTIONS on Information},
title={The Effect of Axis-Wise Triaxial Acceleration Data Fusion in CNN-Based Human Activity Recognition},
year={2020},
volume={E103-D},
number={4},
pages={813-824},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDP7409},
ISSN={1745-1361},
month={April},}
Copier
TY - JOUR
TI - The Effect of Axis-Wise Triaxial Acceleration Data Fusion in CNN-Based Human Activity Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 813
EP - 824
AU - Xinxin HAN
AU - Jian YE
AU - Jia LUO
AU - Haiying ZHOU
PY - 2020
DO - 10.1587/transinf.2018EDP7409
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2020
AB - 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.
ER -