36,712 research outputs found
Deep HMResNet Model for Human Activity-Aware Robotic Systems
Endowing the robotic systems with cognitive capabilities for recognizing
daily activities of humans is an important challenge, which requires
sophisticated and novel approaches. Most of the proposed approaches explore
pattern recognition techniques which are generally based on hand-crafted
features or learned features. In this paper, a novel Hierarchal Multichannel
Deep Residual Network (HMResNet) model is proposed for robotic systems to
recognize daily human activities in the ambient environments. The introduced
model is comprised of multilevel fusion layers. The proposed Multichannel 1D
Deep Residual Network model is, at the features level, combined with a
Bottleneck MLP neural network to automatically extract robust features
regardless of the hardware configuration and, at the decision level, is fully
connected with an MLP neural network to recognize daily human activities.
Empirical experiments on real-world datasets and an online demonstration are
used for validating the proposed model. Results demonstrated that the proposed
model outperforms the baseline models in daily human activity recognition.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Deep neural networks, including recurrent networks, have been successfully
applied to human activity recognition. Unfortunately, the final representation
learned by recurrent networks might encode some noise (irrelevant signal
components, unimportant sensor modalities, etc.). Besides, it is difficult to
interpret the recurrent networks to gain insight into the models' behavior. To
address these issues, we propose two attention models for human activity
recognition: temporal attention and sensor attention. These two mechanisms
adaptively focus on important signals and sensor modalities. To further improve
the understandability and mean F1 score, we add continuity constraints,
considering that continuous sensor signals are more robust than discrete ones.
We evaluate the approaches on three datasets and obtain state-of-the-art
results. Furthermore, qualitative analysis shows that the attention learned by
the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable
Computers (ISWC) 201
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