Real-Time Short-Term Pedestrian Trajectory Prediction Based on Gait Biomechanics

Sensors (Basel). 2022 Aug 4;22(15):5828. doi: 10.3390/s22155828.


The short-term prediction of a person's trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton's laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with the foundations of the basic kinematical models compromising their performance. In this paper, we propose a short-time prediction method based on gait biomechanics for real-time applications. This method relays on a single biomechanical variable, and it has a low computational burden, turning it into a feasible solution to implement in low-cost portable devices. We evaluate its performance from an experimental benchmark where several subjects walked steadily over straight and curved paths. With this approach, the results indicate a performance good enough to be applicable to a wide range of human-robot interaction applications.

Keywords: gait biomechanics; kinematical models; motion trajectory prediction.

MeSH terms

  • Biomechanical Phenomena
  • Gait
  • Humans
  • Motion
  • Pedestrians*
  • Walking

Grants and funding

This research received no external funding.