Summary: This project builds a pipeline from force measurement to posture prediction. We’re currently designing and fabricating the measurement setup and data‑logging system.
Project Context & Goal
- Why: Predicting posture under static pushing/pulling can inform ergonomic design and reduce musculoskeletal risk.
- Objective: Train an artificial neural network that maps measured static forces to joint postures estimated with OpenSim.
- Scope: Hardware setup → data logging → biomechanical feature extraction → ANN training/validation.
Process Roadmap
Now
1) Force Measurement Setup
CAD will be added when ready
- Design & manufacture the mechanical rig for pushing/pulling.
- Integrate strain gauges; select DAQ; amplifier wiring & calibration.
- Static load cases & calibration curve generation.
2) Data Collection
Add test case photos / screenshots
- Implement robust data logging (timestamping, units, metadata).
- Define test cases; record steady static forces for each posture.
- Optional: synchronize with motion capture or reference posture measures.
3) Biomechanical Analysis (OpenSim)
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- Process trials in OpenSim; extract joint angles & posture descriptors.
- Assemble a clean dataset (features/labels) for model training.
- Train/validation split and normalization strategy.
Next
4) ANN Model Development
Add model diagram & results later
- ANN architecture selection; regularization & early stopping.
- Performance metrics (MAE for joint angles, posture error heatmaps).
- Generalization checks on unseen test cases.