A Data-driven Recommendation Framework for Optimal Walker Designs
Advaith Narayanan
The rapidly advancing fields of statistical modeling and machine learning
have significantly enhanced data-driven design and optimization. This paper
focuses on leveraging these design algorithms to optimize a medical walker, an
integral part of gait rehabilitation and physiological therapy of the lower
extremities. To achieve the desirable qualities of a walker, we train a
predictive machine-learning model to identify trade-offs between performance
objectives, thus enabling the use of efficient optimization algorithms. To do
this, we use an Automated Machine Learning model utilizing a stacked-ensemble
approach shown to outperform traditional ML models. However, training a
predictive model requires vast amounts of data for accuracy. Due to limited
publicly available walker designs, this paper presents a dataset of more than
5,000 parametric walker designs with performance values to assess mass,
structural integrity, and stability. These performance values include
displacement vectors for the given load case, stress coefficients, mass, and
other physical properties. We also introduce a novel method of systematically
calculating the stability index of a walker. We use MultiObjective
Counterfactuals for Design (MCD), a novel genetic-based optimization algorithm,
to explore the diverse 16-dimensional design space and search for
high-performing designs based on numerous objectives. This paper presents
potential walker designs that demonstrate up to a 30% mass reduction while
increasing structural stability and integrity. This work takes a step toward
the improved development of assistive mobility devices.