Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Vertical Ground Reaction Forces for Given Human Standing Posture With Uneven Terrains: Prediction and Validation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Yang, J. ; Dept. of Mech. Eng., Texas Tech Univ., Lubbock, TX, USA ; Howard, B. ; Cloutier, A. ; Domire, Z.J.

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%.

Published in:

Human-Machine Systems, IEEE Transactions on  (Volume:43 ,  Issue: 2 )