Application of Gesture Recognition Fusion Algorithm Based on Double LeapMotion in Hand Function Rehabilitation in Large Space

With the development of human-computer interaction technology, due to the advantages of virtual interaction and the quantification of motion capture data, gesture recognition controllers such as LeapMotion have been introduced into hand function rehabilitation training. However, when used individually, its measuring angle and measuring range are limited and cannot be applied in a large space. In this paper, the dual LeapMotion combination is used to unify the data in the two coordinate systems into the same coordinate system, thereby increasing the acquisition space range and space field of view. Through coordinate system conversion, the converted data is consistent with the actual test action. This method can be extended to multiple LeapMotion combinations.