TY - GEN
T1 - Effect of uncertainty on human posture prediction
AU - Li, Ning
AU - Yang, James
N1 - Publisher Copyright:
Copyright © 2014 by ASME.
PY - 2014
Y1 - 2014
N2 - Posture prediction is a key component in digital human modeling and simulation. Deterministic optimization-based posture prediction formulations have been proposed. However, there exist uncertainties in human anthropometric (human height, link length, center of mass of segments, and moment of inertia, etc.) and environment parameters (location, interaction force), which affects the predicted posture. This paper attempts to study the effect of uncertainty on predicted posture. The single-loop reliability based design optimization (RBDO) method is adapted to predict posture under uncertainties. All random parameters are assumed to have normal distribution. A 24-degree of freedom (DOF) upper body model is used. In this pilot study, it is assumed that one link length and one joint angle are random parameters. The other design variables and parameters are deterministic. With the empirical rule, three cases are investigated for posture prediction. SNOPT software solver is employed to solve the optimization problem. Through comparison with deterministic optimization result and experimental data, the predicted postures from RBDO simulation show that the reliability index affects the predicted posture to some extent.
AB - Posture prediction is a key component in digital human modeling and simulation. Deterministic optimization-based posture prediction formulations have been proposed. However, there exist uncertainties in human anthropometric (human height, link length, center of mass of segments, and moment of inertia, etc.) and environment parameters (location, interaction force), which affects the predicted posture. This paper attempts to study the effect of uncertainty on predicted posture. The single-loop reliability based design optimization (RBDO) method is adapted to predict posture under uncertainties. All random parameters are assumed to have normal distribution. A 24-degree of freedom (DOF) upper body model is used. In this pilot study, it is assumed that one link length and one joint angle are random parameters. The other design variables and parameters are deterministic. With the empirical rule, three cases are investigated for posture prediction. SNOPT software solver is employed to solve the optimization problem. Through comparison with deterministic optimization result and experimental data, the predicted postures from RBDO simulation show that the reliability index affects the predicted posture to some extent.
KW - Digital human
KW - Posture prediction
KW - Single-loop RBDO
KW - Stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=84961368763&partnerID=8YFLogxK
U2 - 10.1115/DETC201434232
DO - 10.1115/DETC201434232
M3 - Conference contribution
AN - SCOPUS:84961368763
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 34th Computers and Information in Engineering Conference
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014
Y2 - 17 August 2014 through 20 August 2014
ER -