Development and validation of design tool for concept choice on robustness, reliability and safety criteria

2017 ◽  
Author(s):  
Vinicius Marini ◽  
Saeema Ahmed-kristensen
Keyword(s):  
Design Tool ◽  
Development And Validation

Multi-objective optimization-based method for kinematic posture prediction: development and validation

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 245-253 ◽  
Author(s):  
Jingzhou (James) Yang ◽  
Tim Marler ◽  
Salam Rahmatalla
Keyword(s):  
Performance Measures ◽  
Human Performance ◽  
Design Tool ◽  
Upper Body ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Validation Criteria ◽  
Posture Prediction ◽  
Development And Validation ◽  
Digital Human

SUMMARYPosture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

Development and validation of a new finite element human head model

2018 ◽  
Vol 35 (1) ◽  
pp. 477-496 ◽  
Author(s):  
Fábio A.O. Fernandes ◽  
Dmitri Tchepel ◽  
Ricardo J. Alves de Sousa ◽  
Mariusz Ptak
Keyword(s):  
Finite Element ◽  
Design Methodology ◽  
Human Head ◽  
Design Tool ◽  
Head Model ◽  
Accident Reconstruction ◽  
Research Groups ◽  
Content Type ◽  
Development And Validation ◽  
The Brain

Purpose Currently, there are some finite element head models developed by research groups all around the world. Nevertheless, the majority are not geometrically accurate. One of the problems is the brain geometry, which usually resembles a sphere. This may raise problems when reconstructing any event that involves brain kinematics, such as accidents, affecting the correct evaluation of resulting injuries. Thus, the purpose of this study is to develop a new finite element head model more accurate than the existing ones. Design/methodology/approach In this work, a new and geometrically detailed finite element brain model is proposed. Special attention was given to sulci and gyri modelling, making this model more geometrically accurate than currently available ones. In addition, these brain features are important to predict specific injuries such as brain contusions, which usually involve the crowns of gyri. Findings The model was validated against experimental data from impact tests on cadavers, comparing the intracranial pressure at frontal, parietal, occipital and posterior fossa regions. Originality/value As this model is validated, it can be now used in accident reconstruction and injury evaluation and even as a design tool for protective head gear.

18: Measuring Quality-of-Life after Surgery: Development and Validation of the Convalescence and Recovery Evaluation (CARE)

2007 ◽  
Vol 177 (4S) ◽  
pp. 7-7
Author(s):  
Brent K. Hollenbeck ◽  
J. Stuart Wolf ◽  
Rodney L. Dunn ◽  
Martin G. Sanda ◽  
David P. Wood ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Development And Validation

Development of the Teacher Attitudes Towards Self-Regulated Learning Scale

2018 ◽  
Vol 34 (3) ◽  
pp. 193-205 ◽  
Author(s):  
Julia Steinbach ◽  
Heidrun Stoeger
Keyword(s):  
Factor Analysis ◽  
Primary School ◽  
Factor Structure ◽  
Concurrent Validity ◽  
Binary Logistic Regression ◽  
Affective Component ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Teachers Attitudes ◽  
Development And Validation

Abstract. We describe the development and validation of an instrument for measuring the affective component of primary school teachers’ attitudes towards self-regulated learning. The questionnaire assesses the affective component towards those cognitive and metacognitive strategies that are especially effective in primary school. In a first study (n = 230), the factor structure was verified via an exploratory factor analysis. A confirmatory factor analysis with data from a second study (n = 400) indicated that the theoretical factor structure is appropriate. A comparison with four alternative models identified the theoretically derived factor structure as the most appropriate. Concurrent validity was demonstrated by correlations with a scale that measures the degree to which teachers create learning environments that enable students to self-regulate their learning. Retrospective validity was demonstrated by correlations with a scale that measures teachers’ experiences with self-regulated learning. In a third study (n = 47), the scale’s concurrent validity was tested with scales measuring teachers’ evaluation of the desirability of different aspects of self-regulated learning in class. Additionally, predictive validity was demonstrated via a binary logistic regression, with teachers attitudes as predictor on their registration for a workshop on self-regulated learning and their willingness to implement a seven-week training program on self-regulated learning.

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