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Human activity learning for assistive robotics using a classifier ensemble
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DOI：10.1007/s00500-018-3364-x.png)
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摘要
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Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers-support vector machines (SVMs), K-nearest neighbour and random forest-is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset-the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities.
Keyword:
Human activity learning
Feature extraction
Classifier ensemble
Assistive robotics
Activity classification
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IF:
2.5
论文数:
1.0W
被引数:
2.1W
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Integration of classifier diversity measures for feature selection-based classifier ensemble reduction集成分类器多样性度量，用于基于特征选择的分类器集成约简.png)
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