Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

Authors

Jin Tian, Baichun Wei, Haijiao Wang, Chifu Yang, Sicong Fu, Haiqi Zhu, Chunzhi Yi

Published in

IEEE transactions on bio-medical engineering. Volume PP. Jul 03, 2026. Epub Jul 03, 2026.

Abstract

Semi-active shoulder exoskeletons strike a balance between active and passive exoskeletons for reducing shoulder load during overhead work. Current assistance strategy focuses on assisting with static tasks, neglecting the requirement of dynamic tasks, e.g. overhead wall painting.
This study presents a robust rule-based heuristic assistance strategy for a semi-active shoulder exoskeleton to improve task adaptability. Different from previous studies, the robust rule-based heuristic assistance strategy determines assistance by weighing both angular velocity and displacement to accommodate both static and dynamic tasks. A robust detection mechanism is also proposed to improve its stability under non-uniform and abrupt movements, making it better suited for real-world applications.
Experiments on the outputs of the robust rule-based heuristic assistance strategy showed that minimal support (Level 0) was provided for tasks below the overhead region, whereas increased assistance was offered for overhead tasks (Levels 1 to 3), with static tasks (Level 3) receiving greater support than dynamic ones (Levels 1 and 2). Results showed that applying the robust detection mechanism can achieve a reduction of up to 60.3% in the switching times the assistance level changes. Electromyography experiments showed that the exoskeleton with our strategy reduced muscle activation with reductions of up to 52.4%.
These results indicate that the proposed exoskeleton can provide effective assistance for both static and dynamic overhead tasks.
Our study provides valuable insights into the practical utility and robustness of exoskeletons, thereby accelerating their application in occupational scenarios.

PMID:
42397994
Bibliographic data and abstract were imported from PubMed on 04 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.