Autonomous gesture recognition using multi-layer LSTM networks and laban movement analysis

Abstract

In recent years, due to the reasonable price of RGB-D devices, the use of skeletal-based data in the field of human-computer interaction has attracted a lot of attention. Being free from problems such as complex backgrounds as well as changes in light is another reason for the popularity of this type of data. In the existing methods, the use of joint and bone information has had significant results in improving the recognition of human movements and even emotions. However, how to combine these two types of information in the best possible way to define the relationship between joints and bones is a problem that has not yet been solved. In this article, we used the Laban Movement Analysis (LMA) to build a robust descriptor and present a precise description of the connection of the different parts of the body to itself and its surrounding environment while performing a gesture. To do this, in addition to the distances between the hip center and other joints of the body and the changes of the quaternion angles in time, we define the triangles formed by the different parts of the body and calculate their area. We also calculate the area of the single conforming 3-D boundary around all the joints of the body. We use a long short-term memory (LSTM) network to evaluate this descriptor. The proposed algorithm is implemented on five public datasets: NTU RGB $+$ D 120, SYSU 3D HOI, FLORENCE 3D ACTIONS, MSR Action3D and UTKinect-Action3D datasets, and the results are compared with those available in the literature.