Optimization of Posture Prediction Using MOO in Brick Stacking Operation

2019 ◽  
pp. 461-472 ◽  
Author(s):  
Biswaranjan Rout ◽  
P. P. Tripathy ◽  
R. R. Dash ◽  
D. Dhupal
Keyword(s):  
Posture Prediction

Posture prediction and physics-based human motion simulation

2019 ◽  
pp. 425-440
Author(s):  
Rajan Bhatt ◽  
Kimberly Farrell ◽  
Karim Abdel-Malek ◽  
Jasbir Arora ◽  
Chris Murphy
Keyword(s):  
Human Motion ◽  
Motion Simulation ◽  
Human Motion Simulation ◽  
Posture Prediction

Handwriting posture prediction based on unsupervised model

2020 ◽  
Vol 100 ◽  
pp. 107093
Author(s):  
Bailin Yang ◽  
Yulong Zhang ◽  
Zhenguang Liu ◽  
Xiaoheng Jiang ◽  
Mingliang Xu
Keyword(s):  
Posture Prediction

Physics-Based Seated Posture Prediction for Pregnant Women and Validation Considering Ground and Seat Pan Contacts

2012 ◽  
Vol 134 (7) ◽  
Author(s):  
Bradley Howard ◽  
Aimee Cloutier ◽  
Jingzhou (James) Yang
Keyword(s):  
Pregnant Women ◽  
Objective Function ◽  
Linear Regression Analysis ◽  
Joint Torque ◽  
Joint Torques ◽  
Design Variables ◽  
Optimization Formulation ◽  
Posture Prediction ◽  
Seated Posture ◽  
End Effectors

An understanding of human seated posture is important across many fields of scientific research. Certain demographics, such as pregnant women, have special postural limitations that need to be considered. Physics-based posture prediction is a tool in which seated postures can be quickly and thoroughly analyzed, as long the predicted postures are realistic. This paper proposes and validates an optimization formulation to predict seated posture for pregnant women considering ground and seat pan contacts. For the optimization formulation, the design variables are joint angles (posture); the cost function is dependent on joint torques. Constraints include joint limits, joint torque limits, the distances from the end-effectors to target points, and self-collision avoidance constraints. Three different joint torque cost functions have been investigated to account for the special postural characteristics of pregnant women and consider the support reaction forces (SRFs) associated with seated posture. Postures are predicted for three different reaching tasks in common reaching directions using each of the objective function formulations. The predicted postures are validated against experimental postures obtained using motion capture. A linear regression analysis was used to evaluate the validity of the predicted postures and was the criteria for comparison between the different objective functions. A 56 degree of freedom model was used for the posture prediction. Use of the objective function minimizing the maximum normalized joint torque provided an R2 value of 0.828, proving superior to either of two alternative functions.

Recognition and prediction of driver’s whole body posture model

2022 ◽  
pp. 095440702110686
Author(s):  
Jun Wu ◽  
Jian Liu ◽  
Xiuyuan Li ◽  
Lingbo Yan ◽  
Libo Cao ◽  
...  
Keyword(s):  
Regression Model ◽  
Body Posture ◽  
Upper Body ◽  
Whole Body ◽  
Lower Body ◽  
Key Factor ◽  
Upper Body Posture ◽  
Spatial Coordinates ◽  
Posture Prediction ◽  
Connection Length

The driver’s whole-body posture at the time of a collision is a key factor in determining the magnitude of injury to the driver. However, current researchs on driver posture models only consider the upper body posture of the driver, and the lower body area which is not perceived by sensors is not studied. This paper investigates the driver’s posture and establishes a 3D posture model of the driver’s whole body through the application of machine vision algorithms and regression model statistics. This study proposes an improved Kinect-OpenPose algorithm for identifying the 3D spatial coordinates of nine keypoints of the driver’s upper body. The posture prediction regression model of four keypoints of the lower body is established by conducting volunteer posture acquisition experiments on the developed simulated driving seat and analyzing the volunteer posture data through using the principal components of the upper body keypoints and the seat parameters. The experiments proved that the error of the regression model in this paper is minor than that of current studies, and the accuracy of the keypoint location and the keypoint connection length of the established driver whole body posture model is high, which provides implications for future studies.

scholarly journals A method for posture prediction of the upper trunk of video terminal operators

2016 ◽  
Vol 14 (1) ◽  
pp. 28-37
Author(s):  
Luca Di Angelo ◽  
Paolo Di Stefano
Keyword(s):  
Posture Prediction ◽  
Video Terminal

Posture Prediction Versus Inverse Kinematics

2001 ◽  
Author(s):  
Karim Abdel-Malek ◽  
Wei Yu ◽  
Zan Mi ◽  
E. Tanbour ◽  
M. Jaber
Keyword(s):  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Human Performance ◽  
Inverse Kinematic ◽  
Cost Functions ◽  
Optimization Approach ◽  
Final Position ◽  
Serial Robot ◽  
Posture Prediction

Abstract Inverse kinematics is concerned with the determination of joint variables of a manipulator given its final position or final position and orientation. Posture prediction also refers to the same problem but is typically associated with models of the human limbs, in particular for postures assumed by the torso and upper extremities. There has been numerous works pertaining to the determination and enumeration of inverse kinematic solutions for serial robot manipulators. Part of these works have also been directly extended to the determination of postures for humans, but have rarely addressed the choice of solutions undertaken by humans, but have focused on purely kinematic solutions. In this paper, we present a theoretical framework that is based on cost functions as human performance measures, subsequently predicting postures based on optimizing one or more of such cost functions. This paper seeks to answer two questions: (1) Is a given point reachable (2) If the point is reachable, we shall predict a realistic posture. We believe that the human brain assumes different postures driven by the task to be executed and not only on geometry. Furthermore, because of our optimization approach to the inverse kinematics problem, models with large number of degrees of freedom are addressed. The method is illustrated using several examples.

scholarly journals Mapping Center Of Pressure during Standing Reach Tasks

2019 ◽  
Vol 63 (1) ◽  
pp. 1008-1012
Author(s):  
Monica L. H. Jones ◽  
Sheila M. Ebert ◽  
Clive D’Souza ◽  
Matthew P. Reed
Keyword(s):  
Material Handling ◽  
Target Location ◽  
Center Of Pressure ◽  
Digital Human Modeling ◽  
Foot Placement ◽  
Wide Range ◽  
Modeling Software ◽  
Posture Prediction ◽  
Human Figure ◽  
Digital Human

Postural stability and balance during manual material handling and industrial tasks are fundamental to ergonomic assessment of workplace tasks. Previous research has determined that accurate prediction of a person’s balance maintenance strategy is one of the most important parameters affecting the accuracy of posture prediction algorithms. Digital human modeling has the potential to provide designers with accurate tools to represent human posture, but currently available software typically lacks empirically-derived models of center of pressure (CoP) excursion. This paper presents an overview of a study that systematically quantified CoP excursion behavior through a series of standing reach tasks for participants with a wide range of body size. CoP excursion was greatly affected by foot placement and target location. The overall goal of this research is to develop an empirical model of center of pressure (CoP) excursion that can be integrated into human figure modeling software to improve prediction of standing postures typically observed in industrial tasks.

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