In socially assistive robotics, human activity recognition plays a central role when the adaptation of the robot behavior to the human one is required. In this paper, we present an activity recognition approach for activities of daily living based on deep learning and skeleton data. In the literature, ad hoc features extraction/selection algorithms with supervised classification methods have been deployed, reaching an excellent classification performance. Here, we propose a deep learning approach, combining CNN and LSTM, that exploits both the learning of spatial dependencies correlating the limbs in a skeleton 3D grid representation and the learning of temporal dependencies from instances with a periodic pattern that works on raw data and so without requiring an explicit feature extraction process. These models are proposed for real-time activity recognition, and they are tested on the CAD-60 dataset. Results show that the proposed model behaves better than an LSTM model thanks to the automatic features extraction of the limbs’ correlation. “New Person” results show that the CNN-LSTM model achieves \(95.4\%\) of precision and \(94.4\%\) of recall, while the “Have Seen” results are \(96.1\%\) of precision and \(94.7\%\) of recall.

在社交辅助机器人中，当需要使机器人行为适应人类时，人类活动识别就起着核心作用。在本文中，我们提出了一种基于深度学习和骨架数据的日常生活活动识别方法。在文献中，已经部署了具有监督分类方法的临时特征提取/选择算法，达到了出色的分类性能。在这里，我们提出了一种结合了CNN和LSTM的深度学习方法，该方法既学习与骨骼3D网格表示中的肢体相关的空间依赖关系的学习，又利用来自具有原始数据的周期性模式的实例学习时间依赖关系无需明确的特征提取过程。这些模型被建议用于实时活动识别，并在CAD-60数据集上进行了测试。结果表明，由于肢体相关性的自动特征提取，所提出的模型比LSTM模型表现更好。“新人”结果表明，CNN-LSTM模型达到了\（95.4 \％\）的精度和\（94.4 \％\）的召回率，而“已见”结果是\（96.1 \％\）的精度和（94.7 \％\）的召回率。