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Existing Convolutional Neural Networks (CNNs) based methods for action recognition are either spatial or temporally local while actions are 3D signals.
Unlike many existing methods for action recognition which depend on well-designed features, this paper studies deep learning-based action recognition using depth sequences and the corresponding skeleton joint information.
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This paper explores a new method for action synchronization and control of telerobotic systems.
In [4], Hussein et al. propose a method for action recognition based on the covariance matrix for skeleton joints locations over time.
With large deformations, a simplified calculation method for catenary action and tensile membrane action is proposed.
This finding suggests a possible method of action for retigabine, an M-current enhancer currently under investigation for therapeutic use for its anti-convulsant properties [55].
The successive steps of the model for Accelerating Improvement, a scientific method used for action-oriented learning, were followed and an implementation strategy was developed based on the current evidence on successful implementation of care innovation [ 14, 15].
The successive steps of the model for Accelerating Improvement, a scientific method used for action-oriented learning, were followed and an implementation strategy was developed based on the current evidence on the successful implementation of care innovation [ 11].
A novel method for human action recognition is proposed in this paper.
Niebles et al. [11] present an unsupervised learning method for human action categories.
In this section, we will introduce the proposed histogram-based method for human action recognition.
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