Bedolla, C.; Gonzalez, J.M.; Ortiz, R.; Amezcua, K.; Hernandez Torres, S.I.; Convertino, V.A.; Snider, E.J.
Real-Time Comparison of Machine Learning-Enabled Devices for Measuring Compensatory Reserve Status. Bioengineering 2026, 13, 817.
https://doi.org/10.3390/bioengineering13070817
AMA Style
Bedolla C, Gonzalez JM, Ortiz R, Amezcua K, Hernandez Torres SI, Convertino VA, Snider EJ.
Real-Time Comparison of Machine Learning-Enabled Devices for Measuring Compensatory Reserve Status. Bioengineering. 2026; 13(7):817.
https://doi.org/10.3390/bioengineering13070817
Chicago/Turabian Style
Bedolla, Carlos, Jose M. Gonzalez, Ryan Ortiz, Krysta Amezcua, Sofia I. Hernandez Torres, Victor A. Convertino, and Eric J. Snider.
2026. "Real-Time Comparison of Machine Learning-Enabled Devices for Measuring Compensatory Reserve Status" Bioengineering 13, no. 7: 817.
https://doi.org/10.3390/bioengineering13070817
APA Style
Bedolla, C., Gonzalez, J. M., Ortiz, R., Amezcua, K., Hernandez Torres, S. I., Convertino, V. A., & Snider, E. J.
(2026). Real-Time Comparison of Machine Learning-Enabled Devices for Measuring Compensatory Reserve Status. Bioengineering, 13(7), 817.
https://doi.org/10.3390/bioengineering13070817
