User-Tailored Learning to Forecast Walking Modes for Exosuits

TL;DR

This paper introduces a machine learning-based perception module that accurately estimates walking modes using inertial sensors, enabling adaptive control of exosuits tailored to individual users in real-time.

Contribution

It presents a novel inertial sensor-based perception system for exosuits that supports online adaptation and self-labeling for personalized walking mode recognition.

Findings

High accuracy in walking mode classification

Effective online model adaptation for new users

Successful integration with exosuit control system

Abstract

Assistive robotic devices, like soft lower-limb exoskeletons or exosuits, are widely spreading with the promise of helping people in everyday life. To make such systems adaptive to the variety of users wearing them, it is desirable to endow exosuits with advanced perception systems. However, exosuits have little sensory equipment because they need to be light and easy to wear. This paper presents a perception module based on machine learning that aims at estimating 3 walking modes (i.e., ascending or descending stairs and walking on level ground) of users wearing an exosuit. We tackle this perception problem using only inertial data from two sensors. Our approach provides an estimate for both future and past timesteps that supports control and enables a self-labeling procedure for online model adaptation. Indeed, we show that our estimate can label data acquired online and refine the model for new users. A thorough analysis carried out on real-life datasets shows the effectiveness of our user-tailored perception module. Finally, we integrate our system with the exosuit in a closed-loop controller, validating its performance in an online single-subject experiment.