物体配置を考慮した作業動作における手先位置の早期予測

抄録

In this paper, we propose an early prediction method for work operations to estimate the destination of the hand from the initial motion of the worker. A feature of the work operation is that it consists of grasping actions of parts and tools placed around the worker. Therefore, the positions of these objects are considered to provide effective information for early prediction of actions. In this study, we propose a spatial graph, Human-Object Relational Graph (HORG), which describes the positional relationship between human joints and objects, and apply it to Graph Convolutional Networks. Experimental results using an original dataset consisting of 6,900 grasping actions (about 26k frames) by 23 subjects showed that the proposed method achieved a recognition rate of 66.8% at 0.36 seconds before the end of the motion, which is 8.5% higher than that of the conventional method (ST-GCN). The advantage of our method was also confirmed in terms of the positional accuracy of the hand position prediction.