Towards Fog-Based HiTLCPS for Human Robot Interactions in Smart Lab: Use Cases and Architecture Overview

Karim, Behzad; Tan, Qing; Álvarez, Juan C.

doi:10.1007/978-3-030-57802-2_57

Cited by 1 publication

(1 citation statement)

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“…With that prediction, the possibilities of enhancing intelligent human–robot collaborative environments increase. Robots can plan their motion to adjust their actions better for more efficient and safe collaboration with humans [ 8 ]. The prediction is based on information coming from the monitorisation of the human with environmental sensors, or sensors mounted on the robot, or wearables [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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

Gonzalez¹,

López²,

Álvarez³

et al. 2022

Sensors

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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.

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