In this paper, we propose new deep learning architectures to fuse data provided by multiple sensors. More specifically, we combine classical features extracted from a sensor and raw data of other sensors. In order to make this data fusion possible, we exploited convolution, dense, and concatenation layers. The Mobile HEALTH dataset has been used to support our study. The results show that the proposed architectures are suitable for future use in the Human Activity Recognition (HAR) domain since their performances are comparable or better than those presented in the recent literature and the reference architectures. Indeed, we reached approximately an accuracy of 0.965 and 0.995 for the leave-one-subject-out and train-test strategies, respectively.

在本文中，我们提出了新的深度学习架构，以融合多个传感器提供的数据。更具体地说，我们结合了从传感器中提取的经典特征和其他传感器的原始数据。为了使这种数据融合成为可能，我们利用了卷积层，密集层和串联层。移动健康数据集已用于支持我们的研究。结果表明，所提出的体系结构适合用于人类活动识别（HAR）领域，因为它们的性能与最近的文献和参考体系结构相比具有可比性或更好。的确，对于留一法和训练测试策略，我们分别达到了约0.965和0.995的准确度。