A machine learning approach to k-step look-ahead prediction of gait variables from acceleration data

• Published in: Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society pp. 284 - 387
• Main Authors: Lai, D.T.H., Shilton, A., Charry, E., Begg, R., Palaniswami, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2009
• Subjects: Acceleration; Data mining; Least squares approximation; Least squares methods; Legged locomotion; Linear regression; Machine learning; Neural networks; Root mean square; Training data
• ISSN: 1094-687X
• DOI: 10.1109/IEMBS.2009.5334512

This paper investigates the use of machine learning to predict a sensitive gait parameter based on acceleration information from previous gait cycles. We investigate a k-step look-ahead prediction which attempts to predict gait variable values based on acceleration information in the current gait cy...