Multitask Learning for Multiple Recognition Tasks: A Framework for Lower-limb Exoskeleton Robot Applications

TL;DR

This paper introduces a multitask learning framework for lower-limb exoskeleton robots that simultaneously recognizes user gait phase and terrain, improving data efficiency and model performance through shared knowledge.

Contribution

It presents a novel multitask learning approach that effectively shares knowledge between recognition tasks, reducing data requirements and enhancing accuracy in exoskeleton control.

Findings

Achieved 99.5% accuracy in terrain classification with limited data

Demonstrated improved data efficiency over baseline models

Validated the approach with gait phase recognition and terrain classification tasks

Abstract

To control the lower-limb exoskeleton robot effectively, it is essential to accurately recognize user status and environmental conditions. Previous studies have typically addressed these recognition challenges through independent models for each task, resulting in an inefficient model development process. In this study, we propose a Multitask learning approach that can address multiple recognition challenges simultaneously. This approach can enhance data efficiency by enabling knowledge sharing between each recognition model. We demonstrate the effectiveness of this approach using Gait phase recognition (GPR) and Terrain classification (TC) as examples, the most conventional recognition tasks in lower-limb exoskeleton robots. We first created a high-performing GPR model that achieved a Root mean square error (RMSE) value of 2.345 $\pm$ 0.08 and then utilized its knowledge-sharing backbone feature network to learn a TC model with an extremely limited dataset. Using a limited dataset for the TC model allows us to validate the data efficiency of our proposed Multitask learning approach. We compared the accuracy of the proposed TC model against other TC baseline models. The proposed model achieved 99.5 $\pm$ 0.044% accuracy with a limited dataset, outperforming other baseline models, demonstrating its effectiveness in terms of data efficiency. Future research will focus on extending the Multitask learning framework to encompass additional recognition tasks.