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.

[1]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[2]  Hugo Larochelle,et al.  Modulating early visual processing by language , 2017, NIPS.

[3]  Patrick Olivier,et al.  Feature Learning for Activity Recognition in Ubiquitous Computing , 2011, IJCAI.

[4]  Moritz Tenorth,et al.  Representations for robot knowledge in the KnowRob framework , 2017, Artif. Intell..

[5]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[6]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[7]  Sang Min Yoon,et al.  Human activity recognition from accelerometer data using Convolutional Neural Network , 2017, 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).

[8]  Eric T. Matson,et al.  A semantic approach for enhancing assistive services in ubiquitous robotics , 2016, Robotics Auton. Syst..

[9]  Davide Anguita,et al.  Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine , 2012, IWAAL.

[10]  Zhenyu He,et al.  Activity recognition from acceleration data based on discrete consine transform and SVM , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[11]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[12]  Samuel Berlemont,et al.  3D gesture classification with convolutional neural networks , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[14]  Abdelghani Chibani,et al.  Contextual Knowledge Representation and Reasoning Models for Autonomous Robots , 2017, AAAI Fall Symposia.

[15]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[16]  Abdelghani Chibani,et al.  An evidential fusion approach for activity recognition in ambient intelligence environments , 2013, Robotics Auton. Syst..

[17]  Tapio Seppänen,et al.  Recognizing human motion with multiple acceleration sensors , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[18]  Tim Oates,et al.  Time series classification from scratch with deep neural networks: A strong baseline , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[19]  Séverin Lemaignan,et al.  Artificial cognition for social human-robot interaction: An implementation , 2017, Artif. Intell..

[20]  Faicel Chamroukhi,et al.  Physical Human Activity Recognition Using Wearable Sensors , 2015, Sensors.

[21]  Davide Anguita,et al.  A Public Domain Dataset for Human Activity Recognition using Smartphones , 2013, ESANN.

[22]  Geoffrey Zweig,et al.  The microsoft 2016 conversational speech recognition system , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[23]  Yi Zheng,et al.  Exploiting multi-channels deep convolutional neural networks for multivariate time series classification , 2015, Frontiers of Computer Science.

[24]  Xiaoli Li,et al.  Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition , 2015, IJCAI.

[25]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Sung-Bae Cho,et al.  Human activity recognition with smartphone sensors using deep learning neural networks , 2016, Expert Syst. Appl..

[27]  Peter Willett,et al.  What is a tutorial , 2013 .

[28]  Wan-Young Chung,et al.  High Accuracy Human Activity Monitoring Using Neural Network , 2008, 2008 Third International Conference on Convergence and Hybrid Information Technology.

[29]  Li Chen,et al.  Implementation of a wearerable real-time system for physical activity recognition based on Naive Bayes classifier , 2010, 2010 International Conference on Bioinformatics and Biomedical Technology.

[30]  Abdelghani Chibani,et al.  Towards Semantic Multimodal Emotion Recognition for Enhancing Assistive Services in Ubiquitous Robotics , 2017, AAAI Fall Symposia.

[31]  Sung-Bae Cho,et al.  Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer , 2011, HAIS.