Human Activity Recognition (HAR) based on motion sensors has drawn a lot of
attention over the last few years, since perceiving the human status enables
context-aware applications to adapt their services on users' needs. However,
motion sensor fusion and feature extraction have not reached their full
potentials, remaining still an open issue. In this paper, we introduce
PerceptionNet, a deep Convolutional Neural Network (CNN) that applies a late 2D
convolution to multimodal time-series sensor data, in order to extract
automatically efficient features for HAR. We evaluate our approach on two
public available HAR datasets to demonstrate that the proposed model fuses
effectively multimodal sensors and improves the performance of HAR. In
particular, PerceptionNet surpasses the performance of state-of-the-art HAR
methods based on: (i) features extracted from humans, (ii) deep CNNs exploiting
early fusion approaches, and (iii) Long Short-Term Memory (LSTM), by an average
accuracy of more than 3%.

本文介绍了一种基于深度卷积神经网络的感知网络（PerceptionNet），其应用于多模态时间序列传感器数据中的后期二维卷积，以自动提取用于人类动作识别的有效特征。我们通过在两个公共可用的 HAR 数据集上进行评估来展示我们的方法，表明所提出的模型有效地融合了多模态传感器，并通过超过 3％的平均准确度优于基于：（i）从人体中提取的特征，（ii）利用早期融合方法的深度卷积神经网络和（iii）长期短期记忆（LSTM）的最先进的 HAR 方法。