Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study.

Azar RM, Waters EL, Opong-Duah J, Mendonca RJ, Cacchione PZ, Johnson MJ

DOI: 10.64898/2026.10.28.26366424
Published: 2026 Oct 29
Container: medRxiv : the preprint server for health sciences
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.64898/2026.10.28.26366424,
  title = {Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study.},
  author = {Azar RM and Waters EL and Opong-Duah J and Mendonca RJ and Cacchione PZ and Johnson MJ},
  year = {2026},
  journal = {medRxiv : the preprint server for health sciences},
  doi = {10.64898/2026.10.28.26366424},
  url = {https://doi.org/10.64898/2026.10.28.26366424}
}

RIS

TY  - JOUR
TI  - Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study.
AU  - Azar RM
AU  - Waters EL
AU  - Opong-Duah J
AU  - Mendonca RJ
AU  - Cacchione PZ
AU  - Johnson MJ
PY  - 2026
JO  - medRxiv : the preprint server for health sciences
DO  - 10.64898/2026.10.28.26366424
UR  - https://doi.org/10.64898/2026.10.28.26366424
ER  -

APA

RM, A., EL, W., J, O., RJ, M., PZ, C., & MJ, J. (2026). Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study.. medRxiv : the preprint server for health sciences. https://doi.org/10.64898/2026.10.28.26366424

Source records

pubmed · retrieved 2026-11-10T12:18:42.472Z
europe-pmc · retrieved 2026-11-10T12:18:42.495Z