Cite
A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis.
MLA
McGinnis, Ryan S., et al. “A Machine Learning Approach for Gait Speed Estimation Using Skin-Mounted Wearable Sensors: From Healthy Controls to Individuals with Multiple Sclerosis.” PLoS ONE, vol. 12, no. 6, June 2017, pp. 1–16. EBSCOhost, https://doi.org/10.1371/journal.pone.0178366.
APA
McGinnis, R. S., Mahadevan, N., Moon, Y., Seagers, K., Sheth, N., Jr.Wright, J. A., DiCristofaro, S., Silva, I., Jortberg, E., Ceruolo, M., Pindado, J. A., Sosnoff, J., Ghaffari, R., & Patel, S. (2017). A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis. PLoS ONE, 12(6), 1–16. https://doi.org/10.1371/journal.pone.0178366
Chicago
McGinnis, Ryan S., Nikhil Mahadevan, Yaejin Moon, Kirsten Seagers, Nirav Sheth, John A. Jr.Wright, Steven DiCristofaro, et al. 2017. “A Machine Learning Approach for Gait Speed Estimation Using Skin-Mounted Wearable Sensors: From Healthy Controls to Individuals with Multiple Sclerosis.” PLoS ONE 12 (6): 1–16. doi:10.1371/journal.pone.0178366.