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 classi cation 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, nally 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.Peer ReviewedPreprin
