Skeleton-based motion prediction: A survey

Abstract

Human motion prediction based on 3D skeleton data is an active research topic in computer vision and multimedia analysis, which involves many disciplines, such as image processing, pattern recognition, and artificial intelligence. As an effective representation of human motion, human 3D skeleton data is favored by researchers because it provide resistant to light effects, scene changes, etc. earlier studies on human motion prediction focuses mainly on RBG data-based techniques. In recent years, researchers have proposed the fusion of human skeleton data and depth learning methods for human motion prediction and achieved good results. We first introduced human motion prediction research background and significance in this survey. We then summarized the latest deep learning-based techniques for predicting human motion in recent years. Finally, a detailed paper review and future development discussion are provided.

From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition

“…Analyzing human movement through 3D skeleton data has promising nontrivial applications in high-stake sectors such as healthcare and rehabilitation [ 1 ], security and surveillance [ 2 ], sports and athletics [ 3 ], and human–computer interaction [ 4 ]. Because of this, integrating deep learning in skeleton data analysis requires an understanding of the model’s decision-making processes.…”