TY  - JOUR
PY  - 2022//
TI  - Real-time short-term pedestrian trajectory prediction based on gait biomechanics
JO  - Sensors (Basel)
A1  - González, Leticia
A1  - López, Antonio M.
A1  - Álvarez, Juan C.
A1  - Alvarez, Diego
SP  - e5828
EP  - e5828
VL  - 22
IS  - 15
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s22155828
ID  - ref1
ER  -