  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.
