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.

翻译：统计建模与机器学习领域的快速发展极大地增强了数据驱动的设计与优化能力。本文聚焦于利用这些设计算法优化医用助行器——一种在下肢步态康复与生理治疗中不可或缺的器械。为实现助行器的理想性能，我们训练预测性机器学习模型以识别各性能目标间的权衡关系，从而支持高效优化算法的应用。为此，我们采用基于堆叠集成方法的自动化机器学习模型，该方法已被证明优于传统机器学习模型。然而，训练预测模型需要海量数据以保证准确性。鉴于公开可用的助行器设计数据有限，本文构建了一个包含超过5000种参数化助行器设计及其性能值的数据集，用于评估质量、结构完整性和稳定性。这些性能值包括给定载荷工况下的位移向量、应力系数、质量及其他物理属性。我们还提出了一种系统性计算助行器稳定性指标的新方法。采用基于遗传算法的多目标反事实设计优化算法（MCD），我们探索了16维设计空间，并基于多个目标搜索高性能设计方案。本文展示的潜在助行器设计可减重高达30%，同时增强结构稳定性与完整性。本工作为改善辅助移动设备的开发迈出了重要一步。