Model-Agnostic Meta-Learning for Adaptive Gait Phase and Terrain Geometry Estimation With Wearable Soft Sensors

Abstract

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. Inter-subject and inter-terrain variability, coupled with limited calibration data in real-world deployments, complicate accurate gait estimations. Further nonlinearities arise from fabric-based soft sensors, which improve comfort but introduce hysteresis, placement error, and fabric deformation. 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 enables efficient adaptation to new users with a small amount of calibration data, while maintaining 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.