Deep-Learning Control of Lower-Limb Exoskeletons via simplified Therapist Input

TL;DR

This paper introduces a data-driven control method for lower-limb exoskeletons that simplifies calibration by inferring and modifying gait features through a user interface, enabling adaptive assistance during walking and stair navigation.

Contribution

It presents a novel three-step approach combining probabilistic gait state inference, user-modifiable features, and predictive control for exoskeletons, reducing calibration complexity.

Findings

Successful inference of gait features from sensor data.

Therapist modifications influenced exoskeleton assistance.

Demonstrated adaptability across walking and stair activities.

Abstract

Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of normative walking patterns. Typically, the control of interaction torques in partial-assistance exoskeletons relies on a hierarchical control structure. These approaches require extensive calibration due to the complexity of the controller and user-specific parameter tuning, especially for activities like stair or ramp navigation. To address the limitations of hierarchical control in exoskeletons, this work proposes a three-step, data-driven approach: (1) using recent sensor data to probabilistically infer locomotion states (landing step length, landing step height, walking velocity, step clearance, gait phase), (2) allowing therapists to modify these features via a user interface, and (3) using the adjusted locomotion features to predict the desired joint posture and model stiffness in a spring-damper system based on prediction uncertainty. We evaluated the proposed approach with two healthy participants engaging in treadmill walking and stair ascent and descent at varying speeds, with and without external modification of the gait features through a user interface. Results showed a variation in kinematics according to the gait characteristics and a negative interaction power suggesting exoskeleton assistance across the different conditions.