Time series analysis of inertial-body signals for the extraction of dynamic properties from human gait

Abstract

This paper presents an algorithm for the automatic estimation of spatio temporal gait properties from signals provided by inertial body sensors. The approach is based on time series analysis. Here, a minimum number of body sensor devices is used, which imposes limitations for the automatic extraction of relevant properties of the gait like step length and velocity. The human gait is represented as a dynamical system (DS), which internal states are hidden. Sensor information is interpreted as an observation of a particular trajectory of the DS, from wich a reconstruction space can be obtained. The reconstruction space is then transformed using standard principal components analysis (PCA). From the transformed space, reliable models to estimate step length and velocities are successfully constructed.

DOI: 10.1109/IJCNN.2010.5596663

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Cite this paper

@article{Sam2010TimeSA, title={Time series analysis of inertial-body signals for the extraction of dynamic properties from human gait}, author={Albert Sam{\`a} and Diego E. Pardo and Joan Cabestany and Alejandro Rodr{\'i}guez-Molinero}, journal={The 2010 International Joint Conference on Neural Networks (IJCNN)}, year={2010}, pages={1-5} }