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Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression
In this paper, we propose a two-task graph convolutional network (2T-GCN) to represent skeleton data for HAE tasks involving abnormality detection and quality evaluation.
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Skeleton Focused Human Activity Recognition in RGB Video
The data-driven approach that learns an optimal representation of vision features like skeleton frames or RGB videos is currently a dominant paradigm for activity recognition.
Effective Human Activity Recognition Based on Small Datasets
To do so, we propose a HAR method that consists of three steps: (i) data transformation involving the generation of new features based on transforming of raw data, (ii) feature extraction involving the learning of a classifier based on the AdaBoost algorithm and the use of training data consisting of the transformed features, and (iii) parameter determination and pattern recognition involving the determination of parameters based on the features generated in (ii) and the use of the parameters as training data for deep learning algorithms to be used to recognize human activities.
