Traction force microscopy using temporal information.

Kalman-filter-based traction force microscopy recovers time-varying cellular traction forces more accurately than standard L2-regularization, filtering temporal noise in both numerical simulations and locomoting-cell experiments.

• Why it matters: Inferring force fields from substrate deformation is ill-posed and can amplify measurement noise, so spatial-only denoising may be insufficient for dynamic systems.
• What they did: The approach uses a Kalman filter to assimilate temporal measurements and recover the force field, validated on numerically generated data and then applied to locomoting cells.
• The result: The method is more accurate than standard algorithms (including L2-regularization), enables reliable inference of time-varying traction forces, and supports extensions using added knowledge and direct inference of low-dimensional force features.

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