Recent Development in Human Motion and Gait Prediction

Junwu Zhang, Monroe David Kennedy

Abstract: Human intent is often hard to model and predict. In fields such as robotics and biomechanics, one type of human motion that is important to model is the bipedal walking motion. As a type of legged locomotion, bipedal walking has unique advantages in daily-life environments such as stairs and muddy surfaces, compared to rolling locomotion that are used by many autonomous mobile robots. In this paper, recent developments in predicting bipedal gait dynamics and the corresponding human motion trajectory is presented. Such prediction usually require two main steps: data collection and data analysis. We inspect and compare existing solutions in each of the two steps, summarize the common approaches, and discuss the potential opportunities in the field going forward.

TL;DR: Meta-analysis on data collection and analysis in gait prediction for robotics

Keywords: Humanoid and Bipedal Locomotion, Human and Humanoid Motion Analysis and Synthesis, Gait Prediction, Gait Analysis