Real-Time Comparison of Machine Learning-Enabled Devices for Measuring Compensatory Reserve Status
- DOI
- 10.3390/bioengineering13070817
- Published
- 2026-07-16
- Container
- Bioengineering
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/bioengineering13070817,
title = {Real-Time Comparison of Machine Learning-Enabled Devices for Measuring Compensatory Reserve Status},
author = {Carlos Bedolla and Jose M. Gonzalez and Ryan Ortiz and Krysta Amezcua and Sofia I. Hernandez Torres and Victor A. Convertino and Eric J. Snider},
year = {2026},
journal = {Bioengineering},
doi = {10.3390/bioengineering13070817},
url = {https://doi.org/10.3390/bioengineering13070817}
}RIS
TY - JOUR TI - Real-Time Comparison of Machine Learning-Enabled Devices for Measuring Compensatory Reserve Status AU - Carlos Bedolla AU - Jose M. Gonzalez AU - Ryan Ortiz AU - Krysta Amezcua AU - Sofia I. Hernandez Torres AU - Victor A. Convertino AU - Eric J. Snider PY - 2026 JO - Bioengineering DO - 10.3390/bioengineering13070817 UR - https://doi.org/10.3390/bioengineering13070817 ER -
APA
Bedolla, C., Gonzalez, J. M., Ortiz, R., Amezcua, K., Torres, S. I. H., Convertino, V. A., & Snider, E. J. (2026). Real-Time Comparison of Machine Learning-Enabled Devices for Measuring Compensatory Reserve Status. Bioengineering. https://doi.org/10.3390/bioengineering13070817
Source records
- crossref · retrieved 2026-09-26T15:29:27.402Z