Physiology-informed layered sensing for intelligent human-exoskeleton interaction

TL;DR

This paper introduces a lightweight, multi-layered sensing system embedded in a soft leg sleeve that captures physiological signals for improved control, safety, and personalization in human-exoskeleton interaction.

Contribution

It presents a novel, integrated textile-based sensing architecture that captures skeletal, muscular, and skin deformation data for real-time physiological understanding in wearable robotics.

Findings

Accurate ankle joint moment estimation with RMSE = 0.13 Nm/kg

Real-time metabolic trend classification with 97.1% accuracy

Injury risk detection within 100 ms with 96% recall

Abstract

Wearable exoskeletons hold transformative promise for restoring mobility across diverse users with muscular weakness or other impairments. However, their translation beyond laboratory environments remains limited by sensing systems that capture movement but not underlying physiology. Here, we present a soft, lightweight smart leg sleeve that achieves anatomically aligned, layered multimodal sensing by integrating textile-based surface electromyography (sEMG) electrodes, ultrasensitive textile strain sensors, and inertial measurement units (IMUs). Each sensing modality targets a distinct physiological layer: IMUs track joint kinematics at the skeletal level, sEMG monitors muscle activation at the muscular level, and strain sensors detect skin deformation at the cutaneous level. Together, these sensors provide real-time perception to support three core objectives: controlling personalized assistance, optimizing user effort, and safeguarding against injury risks. The system is skin-conformal, mechanically compliant, and seamlessly integrated with a custom exoskeleton ($<20$~g total sensor and electronics weight). We demonstrate: (1) accurate ankle joint moment estimation (RMSE = 0.13~Nm/kg), (2) real-time classification of metabolic trends (accuracy = 97.1\%), and (3) injury risk detection within 100~ms (recall = 0.96), all validated on unseen users using a leave-one-subject-out protocol. This work establishes a physiology-aligned sensing architecture that reframes exoskeleton perception from motion tracking to real-time physiological decoding, offering a pathway towards intelligent, adaptive, and personalized wearable robotics.