Citation

Waugh JL, Trinh A, Mohammed RR, McIlroy WE, Kulic D, Waugh JL, Trinh A, Mohammed RR, McIlroy WE, Kulic D, Waugh JL, Kulic D, Trinh A, McIlroy WE, Mohammed RR. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2016; 2016: 6146-6149.

Copyright

(Copyright © 2016, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC.2016.7592131

PMID

28227913

Abstract

This paper proposes a novel approach for gait analysis from wearable sensing, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intra-personal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified relative to the estimated gait phase; these events can then be used to calculate gait parameters. The approach is implemented and tested on a human motion dataset where heel impact and toe takeoff events are extracted with an average error of 0.04 cycles.

Language: en