Personalising Crutch Geometries through Bayesian Optimisation

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• Riccardo Conci, University of Cambridge
• Wednesday 24 January 2024, 19:00-20:00
• Lecture Theatre 2, Computer Laboratory, William Gates Building.

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Crutches are optimised for stable motion, but this safety comes at the cost of comfort and speed. In this paper, we employ Gaussian Processes (GPs) and Bayesian Optimisation (BO) as hypothesis generators to find better crutch configurations, which we validate on a physical prototype. We do so by defining a novel loss function indicating the quality of a crutch design which combines subjective metrics (joint pain, instability and effort) and the corresponding objective ones. Finally, we (1) use this methodology to build a more stable, less effortful and less painful personalised crutch design and (2) use the knowledge built by the GP through these experiments to enhance our understanding of the physical dynamics of crutching.

This talk is part of the Foundation AI series.

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