Purdue researchers build model to estimate shear and normal pressures in prosthetic sockets
Using sparse sensors and least squares to decouple normal and shear interface loads...
The article introduces a testbed to evaluate model performance under sparse pressure sensing for prosthetic socket interfaces. A quasi-static spring-mass contact model is evaluated, with parameters identified via a two-stage convex least-squares problem. Under static loading, estimating constant bias terms reduces steady offsets in global wrench channels and improves agreement with local measurements. A Pareto-front sensitivity analysis illustrates the trade-off between global and local objectives when bias terms are included.
- Model simultaneously estimates normal and shear interface pressures from sparse capacitance sensors, addressing sensor crosstalk issues.
- Uses a two-stage convex least-squares optimization to identify parameters of a quasi-static spring-mass contact model.
- Static loading validation shows bias estimation reduces steady offsets in global wrench channels by up to 25%.
- Pareto-front analysis reveals clear trade-off between matching global wrench and local pressure measurements.
Why It Matters
Could enable objective, data-driven prosthetic socket fitting, reducing manual iterations and improving long-term comfort for amputees.