Joint moment estimation for hip exoskeleton control: A generalized moment feature generation method

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 ± 0.0021 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 () for users compared to assist-off condition in level-ground walking.

Keywords

Lower-limb exoskeleton / Hip joint moment estimation / Deep learning

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We sincerely thank all the students in HARLab for their invaluable support of this work, as well as all the participants in our experiments for their time and effort.

This work was supported by National Key R&D Program of China (2024YFC3082800), National Natural Science Foundation of China (52175272), Guangdong Basic and Applied Basic Research Foundation (2024B1515020008 and 2023B1515130007), and Shenzhen Science and Technology Program (KCXFZ20230731093401004, RCYX20231211090345058 and JCYJ20220530114809021).

Data availability

All data needed to evaluate the conclusions are presented in the manuscript and/or the Supplementary Materials. Additional data that support the findings of this study are available from the corresponding author upon reasonable request.

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