TY - GEN
T1 - An alternative formulation for determining weights of joint displacement objective function in seated posture prediction
AU - Zou, Qiuling
AU - Zhang, Qinghong
AU - Yang, Jingzhou
AU - Boothby, Robyn
AU - Gragg, Jared
AU - Cloutier, Aimee
PY - 2011
Y1 - 2011
N2 - The human posture prediction model is one of the most important and fundamental components in digital human models. The direct optimization-based method has recently gained more attention due to its ability to give greater insights, compared to other approaches, as how and why humans assume a certain pose. However, one longstanding problem of this method is how to determine the cost function weights in the optimization formulation. This paper presents an alternative formulation based on our previous inverse optimization approach. The cost function contains two components. The first is the weighted summation of the difference between experimental joint angles and neutral posture, and the second is the weighted summation of the difference between predicted joint angles and the neutral posture. The final objective function is then the difference of these two components. Constraints include (1) normalized weights within limits; (2) an inner optimization problem to solve for the joint angles, where joint displacement is the objective function; (3) the end-effector reaches the target point; and (4) the joint angles are within their limits. Furthermore, weight limits and linear weight constraints determined through observation are implemented. A 24 degree of freedom (DOF) human upper body model is used to study the formulation. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected and a total of 18 target points are designed for this experiment. The results show that using the new objective function in this alternative formulation can greatly improve the accuracy of the predicted posture.
AB - The human posture prediction model is one of the most important and fundamental components in digital human models. The direct optimization-based method has recently gained more attention due to its ability to give greater insights, compared to other approaches, as how and why humans assume a certain pose. However, one longstanding problem of this method is how to determine the cost function weights in the optimization formulation. This paper presents an alternative formulation based on our previous inverse optimization approach. The cost function contains two components. The first is the weighted summation of the difference between experimental joint angles and neutral posture, and the second is the weighted summation of the difference between predicted joint angles and the neutral posture. The final objective function is then the difference of these two components. Constraints include (1) normalized weights within limits; (2) an inner optimization problem to solve for the joint angles, where joint displacement is the objective function; (3) the end-effector reaches the target point; and (4) the joint angles are within their limits. Furthermore, weight limits and linear weight constraints determined through observation are implemented. A 24 degree of freedom (DOF) human upper body model is used to study the formulation. An in-house motion capture system is used to obtain the realistic posture. Four different percentiles of subjects are selected and a total of 18 target points are designed for this experiment. The results show that using the new objective function in this alternative formulation can greatly improve the accuracy of the predicted posture.
KW - Posture prediction
KW - digital human
KW - direct optimization-based posture prediction
UR - http://www.scopus.com/inward/record.url?scp=79960337841&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21799-9_27
DO - 10.1007/978-3-642-21799-9_27
M3 - Conference contribution
AN - SCOPUS:79960337841
SN - 9783642217982
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 231
EP - 242
BT - Digital Human Modeling - Third International Conference, ICDHM 2011, Held as Part of HCI International 2011, Proceedings
T2 - 3rd International Conference on Digital Human Modeling, ICDHM 2011, Held as Part of HCI International 2011
Y2 - 9 July 2011 through 14 July 2011
ER -