基于图神经网络的瑜伽动作多特征融合识别算法

Yoga Kinesis Multi-Feature Fusion Recognition Algorithm Based on Graph Neural Network

针对现有瑜伽动作识别方法不能挖掘动作与形体特征等深层次信息的问题,提出了一种基于多特征融合图神经网络的改进瑜伽动作识别算法,该算法利用瑜伽的动作历史和形体信息,结合多特征融合和图神经网络的优势,通过建模形体和动作之间的关系图,得到形体信息对不同瑜伽动作类别的影响程度,以及历史动作的长时和短时性。在实验中,对比了该方法与其他算法在瑜伽动作识别任务中的表现。结果表明,该方法在准确率、精确率、召回率和F_(1)值等指标上有明显的提高,证明了该瑜伽动作识别算法的有效性。

In view of the problem found in existing yoga kinesis recognition methods for their failure to mine deep level information such as kinesis and physical features,an improved yoga kinesis recognition algorithm,which is based on multi-feature fusion graph neural network,has thus been proposed.This algorithm utilizes the kinesis record and physique information in the process of yoga,with the advantages of multi-feature fusion and graph neural network combined together.By modeling the relationship between kinesis forms and action,the degree of influence of physique information on different yoga kinesis categories,as well as the short duration and long duration of historical actions,can be obtained.In the experiment,a comparison is made between the performance of the proposed method and other algorithms in yoga kinesis recognition tasks.The results show that the proposed method is characterized with a significant improvement in accuracy,accuracy,recall rate and F_(1)value,thus verifying the effectiveness of the Yoga kinesis recognition algorithm.