Analyzing human gait and posture by combining feature selection and kernel methods

This paper evaluates a set of computational algorithms for the automatic estimation of human postures and gait properties from signals provided by an inertial body sensor. The use of a single sensor device imposes limitations for the automatic estimation of relevant properties, like step length and gait velocity, as well as for the detection of standard postures like sitting or standing. Moreover, the exact location and orientation of the sensor is also a common restriction that is relaxed in this study. Based on accelerations provided by a sensor, known as the ‘9×2’, three approaches are presented extracting kinematic information from the user motion and posture. Firstly, a two-phases procedure implementing feature extraction and Support Vector Machine based classification for daily living activity monitoring is presented. Secondly, Support Vector Regression is applied on heuristically extracted features for the automatic computation of spatiotemporal properties during gait. Finally, sensor information is interpreted as an observation of a particular trajectory of the human gait dynamical system, from which a reconstruction space is obtained, and then transformed using standard principal components analysis, finally Support Vector Regression is used for prediction. Daily living Activities are detected and spatiotemporal parameters of human gait are estimated using methods sharing a common structure based on feature extraction and kernel methods. The approaches presented are susceptible to be used for medical purposes.