In the domain of assistive robotics, the significance of effective modeling is well acknowledged. Prior research has primarily focused on enhancing model accuracy or involved the collection of extensive, often impractical amounts of data. While improving individual model accuracy is beneficial, it necessitates constant remodeling for each new task and user interaction. In this paper, we investigate the generalizability of different modeling methods. We focus on constructing the dynamic model of an assistive exoskeleton using six data-driven regression algorithms. Six tasks are considered in our experiments, including horizontal, vertical, diagonal from left leg to the right eye and the opposite, as well as eating and pushing. We constructed thirty-six unique models applying different regression methods to data gathered from each task. Each trained model's performance was evaluated in a cross-validation scenario, utilizing five folds for each dataset. These trained models are then tested on the other tasks that the model is not trained with. Finally the models in our study are assessed in terms of generalizability. Results show the superior generalizability of the task model performed along the horizontal plane, and decision tree based algorithms.

翻译：在辅助机器人领域，有效建模的重要性已得到广泛认可。先前研究主要侧重于提升模型精度，或涉及采集大量、通常不切实际的数据。虽然提高单个模型的精度是有益的，但这需要对每个新任务和用户交互进行持续建模重构。本文研究了不同建模方法的泛化性，聚焦于使用六种数据驱动回归算法构建辅助外骨骼的动力学模型。实验考虑了六种任务，包括水平、竖直、左腿至右眼对角线方向及其反向运动，以及进食和推拉动作。我们针对每个任务采集的数据，应用不同回归方法构建了三十六个独立模型。每个训练模型的性能通过五折交叉验证进行评估，并将这些训练好的模型应用于其未训练的其他任务进行测试。最终，本研究从泛化性角度对模型进行了评估。结果表明，水平面任务模型以及基于决策树的算法展现出优越的泛化性能。