Carter, J. A., Rivadulla, A. R. & Preatoni, E. (2022). A support vector machine algorithm can successfully classify running ability when trained with wearable sensor data from anatomical locations typical of consumer technology. Sports Biomechanics, 23 (11), 2372-2389. Zugriff am 13.11.2023 unter https://doi.org/10.1080/14763141.2022.2027509
APA-Zitierstil (7. Ausg.)Carter, J. A., Rivadulla, A. R., & Preatoni, E. (2022). A support vector machine algorithm can successfully classify running ability when trained with wearable sensor data from anatomical locations typical of consumer technology. Sports Biomechanics, 23(11), 2372-2389.
Chicago-Zitierstil (17. Ausg.)Carter, J. A., A. R. Rivadulla, und E. Preatoni. "A Support Vector Machine Algorithm Can Successfully Classify Running Ability When Trained with Wearable Sensor Data from Anatomical Locations Typical of Consumer Technology." Sports Biomechanics 23, no. 11 (2022): 2372-2389.
MLA-Zitierstil (9. Ausg.)Carter, J. A., et al. "A Support Vector Machine Algorithm Can Successfully Classify Running Ability When Trained with Wearable Sensor Data from Anatomical Locations Typical of Consumer Technology." Sports Biomechanics, vol. 23, no. 11, 2022, pp. 2372-2389.