Validating EV Ergonomics with Digital Human Models and User Trials

The problem

Ergonomic decisions in vehicle design often rely on digital human models placed in CAD. These models make assumptions about posture and comfort that may not hold for Indian body proportions or for two- and three-wheeler riding positions. Without validation, design teams cannot know how much to trust them.

Objectives

• Build or use digital human models of selected percentiles and place them in an EV seating layout.
• Build an adjustable physical buck matching the CAD layout.
• Measure real participants' postures and joint angles in the buck with a motion capture or marker-based photo method.
• Quantify agreement between predicted and measured postures and propose corrections.

What you need

Key concepts

Digital human model

A computer manikin with body segments and joints, sized from anthropometric data, used to check fit, reach and posture in CAD.

Posture prediction

The way a model chooses joint angles to satisfy contact points such as seat, pedals or pegs and grips. Different rules give different postures for the same layout.

Bland-Altman analysis

A method for comparing two measurement methods by plotting their difference against their mean, showing bias and limits of agreement.

Participant-specific manikin

A manikin scaled to an individual participant's measurements, which separates errors from sizing from errors from posture prediction.

Step-by-step guide

1. 1

   Define the study and get approval

   Choose one or two EV seating layouts, for example an electric scooter and an e-rickshaw driver seat. Write research questions on how well manikin postures match real ones, and which joints disagree most. Get approval from the institute ethics committee and prepare consent forms. Plan for a sample covering small and large participants, and state the sample size reasoning. Decide in advance which joints and which measures you will compare, so the analysis is not chosen after seeing the data.

   Checkpoint: Ethics approval, research questions and a sampling plan with a justified target number of participants.

2. 2

   Build the CAD layout

   Model each layout in SOLIDWORKS with seat, handlebar or steering wheel, floorboard or pedals as the contact surfaces, with positions driven by global variables. Include adjustment ranges you will build into the buck. Make a drawing of each layout with the reference points used for posture measurement, such as seat reference point and grip centre. Save every layout in a design table so the buck settings and the CAD always use the same numbers. Add marker target points on the contact surfaces.

   Checkpoint: A parametric layout for each vehicle with a drawing of reference points and adjustment ranges.

3. 3

   Build the 3D manikin

   Build a manikin as an assembly of simple segment parts: feet, shanks, thighs, pelvis, trunk, upper arms, forearms, hands and head, joined with concentric mates and angle mates with limits from published joint ranges of motion. Drive segment lengths from a design table. Define the posture rule you use: contacts at seat, grips and feet, then a stated preferred trunk angle. If available, prepare the same layout in a dedicated digital human package.

   Checkpoint: A manikin that solves into a posture for each percentile, with segment lengths matching the design table and joint limits respected.

4. 4

   Build and calibrate the buck

   Build an adjustable buck from drawings with seat, grips or steering wheel and foot supports at the CAD positions. Measure the built positions with a tape and a level, or with the motion capture system, and record differences from CAD. Add marker positions on the buck for the reference points so measurements share one coordinate system with the CAD. Recheck the buck positions at the start of each session, since adjustments and handling can move it.

   Checkpoint: Measured buck reference points agree with CAD within a stated tolerance, for example 5 mm.

5. 5

   Collect participant data

   For each participant, record stature, segment lengths with an anthropometer and body mass. Place markers on bony landmarks such as ankle, knee, hip, shoulder, elbow and wrist. Ask them to sit or ride in the buck in their natural position, then in a few prescribed positions. Record marker positions with motion capture or calibrated cameras, repeating each trial to check repeatability.

   Checkpoint: A complete dataset per participant with anthropometry, marker positions and repeat trials.

6. 6

   Compute measured joint angles

   Write a script in MATLAB, Python or Excel that reads marker positions and computes joint angles such as knee, hip, elbow, shoulder and trunk-to-vertical in the buck coordinate system. Estimate measurement error from repeat trials and from marker placement differences. Express angles with the same definitions used in the CAD manikin so the comparison is like for like. Check the script on a manikin pose with known angles before using it on participant data.

   Checkpoint: Joint angles for every participant with a stated repeatability, for example the spread between repeat trials.

7. 7

   Run participant-specific manikins

   For each participant, create a manikin configuration with their own segment lengths and solve it in the CAD layout with your posture rule. Read joint angles from the CAD with angle dimensions. Also run the standard percentile manikins. This separates error from body sizing, which the participant-specific manikin removes, from error in the posture rule itself. Keep a log of manikins that failed to solve and why, since a posture rule that cannot find a pose is itself a finding.

   Checkpoint: Predicted joint angles for every participant-specific manikin and for the percentile manikins.

8. 8

   Analyse agreement

   Compare predicted and measured angles joint by joint using mean difference, root-mean-square error and Bland-Altman plots with limits of agreement. Check whether errors depend on body size, vehicle type or joint. Compare your SOLIDWORKS manikin and, if used, the dedicated package. Test whether differences are larger than measurement error. Use plots of difference against participant stature to see whether error grows with body size. Report results separately for each vehicle layout, because riding postures differ from seated driving.

   Checkpoint: Agreement statistics and Bland-Altman plots for each joint, with bias and limits of agreement stated.

9. 9

   Propose and test corrections

   Use the results to adjust the posture rule, for example a different preferred trunk angle or knee rule for scooter riders. Fit corrections on half the participants and test them on the other half to avoid overfitting. Report whether the corrected rule reduces error on the test group and by how much. Keep the split random but balanced for body size, so both halves include small and large participants. Report the correction in a form designers can apply in CAD.

   Checkpoint: A corrected posture rule that reduces error on a held-out group, with before and after statistics.

10. 10

    Write up

    Write the thesis chapter or paper with the method, buck calibration, measurement error, agreement results, corrections and limits, such as a static buck, marker placement error and the sample. State clearly what designers can and cannot trust from the manikin for these vehicle types. Share anonymised data where the ethics approval allows. Include a short guide with the corrected posture rule and the joints where designers should be cautious. List participant demographics in summary form only.

    Checkpoint: A report with agreement results, a validated correction and a clear statement of limits.

Testing & validation

• Buck positions calibrated against CAD.
• Measurement repeatability estimated from repeat trials.
• Agreement measured with Bland-Altman analysis per joint.
• Corrections tested on a held-out participant group.

Expected results

A quantified comparison of digital human posture predictions with measured postures of real users in EV seating layouts, joint-by-joint agreement statistics and a validated correction to the posture rule.

Take it further

• Add dynamic riding trials on a vibration platform.
• Extend to comfort ratings and relate them to joint-angle error.
• Repeat with a larger, more diverse participant group.

Viva and review questions

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