The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random subspace methods to create different samples and features for improved classification performance of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs.

将骨架数据用于人体姿势识别是人机交互领域的关键研究主题。为了提高人体姿势识别的准确性，提出了一种基于多种特征和规则学习的新算法。首先，定义了包括角度特征和距离特征的219维向量。具体来说，角度和距离特征是根据关节之间的局部关系和关节的整体空间位置定义的。然后，在人体姿势分类中，将规则学习方法与Bagging和随机子空间方法一起使用，以创建不同的样本和特征，以提高子分类器对不同样本的分类性能。最后，在四个人体姿态数据集上评估了我们提出的算法的性能。