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IE 7315 Human Factors Engineering
创建日期：2024-04-18 15:35:15
标签： IE 7315
Human Factors Engineering

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IE 7315 Human Factors Engineering 

Engineering Anthropometry Introduction: 

To design any new product, it is important for the engineer to understand key characteristics of their target population. A field that can be useful is Anthropometry, which involves the scientific study of the measurements and proportions of the human body. This experiment examines regression equations regarding human anthropometric data, based on the methodology established by Karl H.E. Kroemer. During the lab, students will measure various body dimensions to use for the regression analysis. Students will formulate their own equations using a Multiple Linear Regression (MLR) on class data. MLR is a statistical model that assumes that a dependent variable can be estimated by decomposition into a weighted sum of causal factors plus an error term which takes the form: Objectives: ● Understand the application of ergonomics in product design ● Use appropriate techniques and equipment to take accurate body measurements ● Learn how to use correlation and perform data analysis utilizing MLR Apparatus: 60” Flexible Measuring Tape, 12’ Tape Measure, Ruler, Weight Scale Methods: 1. Measure and record the body dimensions listed in Table 1 below. 2. Send page 2 of this lab instruction only in a word document on Canvas. 3. The data will be compiled and distributed to all students right after TA receives all data. 4. Finish and upload the lab 3 report. 2 Table 1 Form for data recording: Body Measurements Unit of Measurement No. in Figure Subject Value Stature Height cm 1 Eye height (standing) cm 2 Shoulder Circumference cm 3 Chest Circumference cm 4 Chest Depth cm 5 Sitting Height cm 6 Eye height (sitting) cm 7 Elbow Rest Height Sitting cm 8 Popliteal Height cm 9 Buttock-Knee Length cm 10 Weight kg --- Gender assignment at your birth The figure numbers listed in Table 1 correspond with the descriptions and images on the page 3. 3 Anthropometric Measurements Facilitator 1. Stature/Stature height: Stand up straight with heels and back against a wall. Measure the distance from the top of head to the floor. 2. Eye Height (standing): Stand up straight with heels and back against a wall. Measure from the bridge of the nose (in line with pupils) to the floor. 3. Shoulder Circumference: Stand up straight with arms resting beside body. Measure circumference around shoulders 4. Chest Circumference: Wrap a flexible measuring tape level around the torso, parallel to the ground and in line with the nipples. 5. Chest Depth: Sit up straight on a level surface with their buttocks and back against a wall. Place a ruler flat across their chest. Measure the distance between the ruler and the wall. 6. Sitting height: Sit up straight on a level surface and measure the distance from top of head to the level surface. 7. Eye Height (sitting): Sit up straight on a level surface. Measure from the bridge of the nose (in line with the pupils) to the desk. 8. Elbow Rest Height Sitting: Sit up straight on a level surface. Bend arm at a 90 degree angle. Measure the distance from bottom of elbow to the level surface. 9. Popliteal Height: Do a “wall sit” against the wall. Measure from the floor to the underside of the leg at a point approximately 5 inches from the back of the knee. 10. Buttock-Knee Length: Sit up straight on a level surface. Measure from the back of the buttocks to the front of the knee. Other Measurements: Not shown in Figures Weight: Weight should be recorded in pounds (lbs). 4 Data Analysis Make sure that dataset is separated on different genders. Select one gender’s dataset based on your gender at your birth, and complete step 1 to 4. 1. Develop a correlation coefficient matrix using Excel or Minitab. Helpful links are provided. 2. Based on the correlation coefficients, identify variables that would be good candidates for dependent variables. See “0.7 convention” at page 15 of the reference paper. 3. Select three different body measurements to use as dependent variables, and iteratively run a multiple linear regression on each one to create the most significant equations possible. An example regression equation is given in Table 2. Table 2 Example regression equation Variable predicted Equation Std Error of Estimate Resulting Correlation (R2) Male Eye height, standing = 0.99*(Stature Height) + 0.08*(Chest Circumference) – 17.16 1.63 0.91  4. Input the values of the measurements you took in class into the prediction equations developed in step 3. Compare this result with the measurement for the same variable that you took in class. 

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