Promoting active participation in robot-aided rehabilitation via machine learning and impedance control
Closing the robot's control loop by adapting to ALP effectively enhanced human-robot interaction and motivated participants to engage more actively in their therapy. These findings suggest that ALP-driven control strategies may improve user involvement in robot-assisted rehabilitation, warranting further investigation in clinically relevant settings.
Authors
- Bram Vanderborght (ORCID: https://orcid.org/0000-0003-4881-9341)
- Kevin Langlois (ORCID: https://orcid.org/0000-0002-7991-0976)
- Loredana Zollo (ORCID: https://orcid.org/0000-0002-8015-010X)
- David Beckwée (ORCID: https://orcid.org/0000-0001-9951-9993)
- Eva Swinnen (ORCID: https://orcid.org/0000-0002-3771-9479)
- Christian Tamantini (ORCID: https://orcid.org/0000-0001-6238-2241)
- Parham Haji Ali Mohamadi (ORCID: https://orcid.org/0009-0001-9522-5987)
- Tom Verstraten (ORCID: https://orcid.org/0000-0001-7398-5398)
- Joris De Winter (ORCID: https://orcid.org/0000-0002-5818-7539)
Institutions
- Vrije Universiteit Brussel (BE)
- Università Campus Bio-Medico (IT)
- IMEC (BE)
- Flanders Make (Belgium) (BE)
- National Research Council (IT)
- Institute of Cognitive Sciences and Technologies (IT)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2025-02-21
- DOI
- https://doi.org/10.3389/fdgth.2025.1559796
- Citations
- 6
- Primary Topic
- Stroke Rehabilitation and Recovery
- Type
- article
- Field-Weighted Citation Impact
- 6.16