Joint Moment Estimation for Hip Exoskeleton Control: A Generalized Moment Feature Generation Method

TL;DR

This paper introduces a generalized moment feature-based method for estimating hip joint moments during walking, improving accuracy and personalization for exoskeleton control using neural networks and invariant features.

Contribution

It proposes a novel GMF generator and a GRU-based neural network for personalized, accurate joint moment estimation using minimal sensor data.

Findings

Achieved 0.1180 Nm/kg RMSE in joint moment estimation across 28 walking speeds.

Improved estimation accuracy by 6.5% over models without body parameter fusion.

Reduced users' metabolic cost by 20.5% with the exoskeleton in level-ground walking.

Abstract

Hip joint moments during walking are the key foundation for hip exoskeleton assistance control. Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation, such as simple sensor requirements and adaptability to variable walking speeds. However, existing joint moment estimation methods still suffer from a lack of personalization, leading to estimation accuracy degradation for new users. To address the challenges, this paper proposes a hip joint moment estimation method based on generalized moment features (GMF). A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder. Utilizing this well-featured representation, a GRU-based neural network is used to predict GMF with joint kinematics data, which can easily be acquired by hip exoskeleton encoders. The proposed estimation method achieves a root mean square error of 0.1180 Nm/kg under 28 walking speed conditions on a treadmill dataset, improved by 6.5% compared to the model without body parameter fusion, and by 8.3% for the conventional fusion model with body parameter. Furthermore, the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5% metabolic reduction (p<0.01) for users compared to assist-off condition in level-ground walking.