Rehman, R.Z.U.; Zhou, Y.; Del Din, S.; Alcock, L.; Hansen, C.; Guan, Y.; Hortobágyi, T.; Maetzler, W.; Rochester, L.; Lamoth, C.J.C.
Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders. Sensors 2020, 20, 6992.
https://doi.org/10.3390/s20236992
AMA Style
Rehman RZU, Zhou Y, Del Din S, Alcock L, Hansen C, Guan Y, Hortobágyi T, Maetzler W, Rochester L, Lamoth CJC.
Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders. Sensors. 2020; 20(23):6992.
https://doi.org/10.3390/s20236992
Chicago/Turabian Style
Rehman, Rana Zia Ur, Yuhan Zhou, Silvia Del Din, Lisa Alcock, Clint Hansen, Yu Guan, Tibor Hortobágyi, Walter Maetzler, Lynn Rochester, and Claudine J. C. Lamoth.
2020. "Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders" Sensors 20, no. 23: 6992.
https://doi.org/10.3390/s20236992
APA Style
Rehman, R. Z. U., Zhou, Y., Del Din, S., Alcock, L., Hansen, C., Guan, Y., Hortobágyi, T., Maetzler, W., Rochester, L., & Lamoth, C. J. C.
(2020). Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders. Sensors, 20(23), 6992.
https://doi.org/10.3390/s20236992
