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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.06207 |
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Table of Contents:
- Back-support exoskeletons (BSEs) mitigate musculoskeletal strain, yet their efficacy depends on precise, context-aware modulation. This paper introduces a user-centric optimization framework and a vision-based adaptive control strategy for industrial BSEs. First, we constructed a multi-metric optimization space, integrating electromyography reduction, perceived discomfort, and user preference, through baseline experiments with 12 subjects. This revealed a non-linear relationship between optimal assistance and payload. Second, we developed a predictive computer vision pipeline using a Vision Transformer (DINOv2) to estimate payloads before lifting, effectively overcoming actuation latency. Validation with 12 subjects confirmed the system's robustness, achieving over 82% estimation accuracy. Crucially, the adaptive controller reduced peak back muscle activation by up to 23% compared to static baselines while optimizing user comfort. These results validate the proposed framework, demonstrating that pre-lift environmental perception and user-centric optimization significantly enhance physical assistance and human-robot interaction in industrial settings.