Online Learning of Gait Models for Fall Prediction

Meaningful parameters can be extracted from data that describes a person walking as time progresses. Gait asymmetry is one such parameter that has been shown to be correlated to the likelihood of a person falling. More generally, the variability in gait parameters can be used for human fall prediction. Currently, models used to describe gait cycles do not generalize well to new variable data. The proposed method learns a model of the gait cycle during online measurement, using a rich and continuous representation that can adapt to inter and intrapersonal variability by creating an individualized model. We expect this model will estimate more accurate gait parameters for improved fall prediction. Furthermore, we predict the evolution of patient models could be used for early detection and assessment of other health conditions.

Faculty Supervisor:

Dana Kulic

Student:

Partner:

INRIA

Discipline:

Engineering

Sector:

University:

University of Waterloo

Program:

Globalink Research Award