DVS Edition Citation

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 (7th ed.) Citation

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 Style (17th ed.) Citation

Carter, J. A., A. R. Rivadulla, and 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 (9th ed.) Citation

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.