This letter presents a model-agnostic meta-learning (MAML) based framework for simultaneous and accurate estimation of human gait phase and terrain geometry using a small set of fabric-based wearable soft sensors, with efficient adaptation to unseen subjects and strong generalization across different subjects and terrains. Compared to rigid alternatives such as inertial measurement units, fabric-based soft sensors improve comfort but introduce nonlinearities due to hysteresis, placement error, and fabric deformation. Moreover, inter-subject and inter-terrain variability, coupled with limited calibration data in real-world deployments, further complicate accurate estimation. To address these challenges, the proposed framework integrates MAML into a deep learning architecture to learn a generalizable model initialization that captures subject- and terrain-invariant structure. This initialization enables efficient adaptation (i.e., adaptation with only a small amount of calibration data and a few fine-tuning steps) to new users, while maintaining strong generalization (i.e., high estimation accuracy across subjects and terrains). Experiments on nine participants walking at various speeds over five terrain conditions demonstrate that the proposed framework outperforms baseline approaches in estimating gait phase, locomotion mode, and incline angle, with superior accuracy, adaptation efficiency, and generalization.

翻译：本文提出了一种基于模型无关元学习（MAML）的框架，利用少量基于织物的可穿戴软传感器，实现对人体步态相位和地形几何的同时精确估计。该框架能高效适应未见过的受试者，并在不同受试者和地形间展现出强大的泛化能力。与惯性测量单元等刚性替代方案相比，基于织物的软传感器提高了舒适度，但由于迟滞、放置误差和织物变形引入了非线性。此外，受试者间和地形间的差异性，加上实际部署中有限的校准数据，进一步增加了精确估计的难度。为应对这些挑战，所提出的框架将MAML集成到深度学习架构中，以学习一个可泛化的模型初始化，该初始化捕获了与受试者和地形无关的结构。此初始化使得模型能够高效适应（即仅需少量校准数据和几步微调）新用户，同时保持强大的泛化能力（即在受试者和地形间均具有高估计精度）。在九名参与者以不同速度在五种地形条件下行走的实验表明，所提框架在估计步态相位、运动模式和倾斜角方面优于基线方法，具有更高的精度、适应效率和泛化性能。