Vertical ground reaction forces for given human standing posture with uneven terrains: Prediction and validation

James Yang, Bradley Howard, Aimee Cloutier, Zachary J. Domire

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Ground reaction forces (GRFs) on individual support vary with posture and motion for bipedal mechanisms or systems due to the redundancy in the system. In digital human modeling, specifically posture prediction, the GRFs are predicted, as they are unknown in a virtual environment. Traditionally, models in which the GRFs are predicted have been presented; however, they are always assumed to be on flat ground. Little work has been done to predict the GRFs on uneven or arbitrary terrain. This paper presents a generic method to calculate the vertical GRFs for given standing postures with uneven terrain. The vertical GRFs are predicted based on the generalized forces (torque in revolute joints; force in prismatic joints) calculated using the recursive Lagrangian formulation and a 3-D zero moment point. Motion capture experiments were used to obtain postures for common standing reaching tasks. Force plates were employed to record GRF information for each task. Experimental postures were reconstructed, and the GRF prediction algorithm was used to predict the associated vertical GRFs for each task. Experimental and predicted vertical GRFs are compared to validate the prediction model. The prediction method proved to be valid, with an overall error of 6%.

Original languageEnglish
Article number6461536
Pages (from-to)225-235
Number of pages11
JournalIEEE Transactions on Human-Machine Systems
Volume43
Issue number2
DOIs
StatePublished - Mar 2013

Keywords

  • Digital human model
  • Ground reaction forces (GRFs)
  • Standing posture
  • Uneven terrain

Fingerprint Dive into the research topics of 'Vertical ground reaction forces for given human standing posture with uneven terrains: Prediction and validation'. Together they form a unique fingerprint.

Cite this