Real-time human action classification using a dynamic neural model
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Summary
A new supervised MTRNN model is proposed for handling the issue of action classification by defining a group of slow context nodes as "classification nodes" and providing both prediction and classification outputs simultaneously during testing.
- Type
- article
- Published
- 2015-09-01
- Cited by
- 22
- References
- 25
- OpenAlex
- https://openalex.org/W275750519
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:19492452
Keywords
Computer science, Artificial intelligence, Robustness (evolution), Artificial neural network, Machine learning
References
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- Kinect and RGBD Images: Challenges and Applications
- Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment
- Imitating others by composition of primitive actions: A neuro-dynamic model
- Original Contribution: Approximation of dynamical systems by continuous time recurrent neural networks
- Achieving "organic compositionality" through self-organization: Reviews on brain-inspired robotics experiments
- Parietal Lobe: From Action Organization to Intention Understanding
- Adaptive neural oscillator using continuous-time back-propagation learning
- Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network
- Neuro-robotics study on integrative learning of proactive visual attention and motor behaviors
- The Observation and Execution of Actions Share Motor and Somatosensory Voxels in all Tested Subjects: Single-Subject Analyses of Unsmoothed fMRI Data
- A survey on vision-based human action recognition
- CREATING NOVEL GOAL-DIRECTED ACTIONS AT CRITICALITY: A NEURO-ROBOTIC EXPERIMENT
- Pretense and representation: The origins of "theory of mind."
- Dynamic Gesture Recognition
Cited by
- Human motion segmentation and recognition using machine vision for mechanical assembly operation
- A limit-cycle self-organizing map architecture for stable arm control
- A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network
- Understanding human intention by connecting perception and action learning in artificial agents
- Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding
- Quantum-Inspired Stacked Auto-encoder-Based Deep Neural Network Algorithm (Q-DNN)
- Multivariate LSTM-FCNs for Time Series Classification
- A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations
- On the robustness of skeleton detection against adversarial attacks
- Multivariate Time Series Classification with Hierarchical Variational Graph Pooling
- A new framework for classification of multi-category hand grasps using EMG signals
- Merging computational fluid dynamics and machine learning to reveal animal migration strategies
- A Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing Data
- Research on Classification Algorithm Based on Multivariate Time Series
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations (Preprint)
- Attentional Gated Res2Net for Multivariate Time Series Classification
- A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram
- Real-Time Neural Classifiers for Sensor Faults in Three Phase Induction Motors
- Time and frequency-domain feature fusion network for multivariate time series classification
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