Optimizing hip exoskeleton assistance pattern based on machine learning and simulation algorithms: a personalized approach to metabolic cost reduction
These simulated predictions, though not empirical measurements, demonstrate the framework's ability to streamline algorithm selection, reduce experimental burden, and accelerate translation of exoskeleton optimization into rehabilitation, occupational, and performance enhancement applications with broader biomechanical and clinical impact.
Authors
- Sara A. Myers (ORCID: https://orcid.org/0000-0002-2934-2624)
- Arash Mohammadzadeh Gonabadi (ORCID: https://orcid.org/0000-0002-4535-0325)
- Iraklis I. Pipinos (ORCID: https://orcid.org/0000-0001-6873-6346)
- Farahnaz Fallahtafti (ORCID: https://orcid.org/0000-0002-4210-2131)
Institutions
- University of Nebraska at Omaha (US)
- Madonna Rehabilitation Hospital (US)
- Omaha VA Medical Center (US)
- VA Nebraska Western Iowa Health Care System (US)
- University of Nebraska Medical Center (US)
Publication Details
- Journal
- Frontiers in Robotics and AI
- Published
- 2025-09-24
- DOI
- https://doi.org/10.3389/frobt.2025.1669600
- Citations
- 2
- Primary Topic
- Prosthetics and Rehabilitation Robotics
- Type
- article
- Field-Weighted Citation Impact
- 1.35