Title
Using frequency analysis to improve the precision of human body posture algorithms based on Kalman filters
Abstract
With the advent of miniaturized inertial sensors many systems have been developed within the last decade to study and analyze human motion and posture, specially in the medical field. Data measured by the sensors are usually processed by algorithms based on Kalman Filters in order to estimate the orientation of the body parts under study. These filters traditionally include fixed parameters, such as the process and observation noise variances, whose value has large influence in the overall performance. It has been demonstrated that the optimal value of these parameters differs considerably for different motion intensities. Therefore, in this work, we show that, by applying frequency analysis to determine motion intensity, and varying the formerly fixed parameters accordingly, the overall precision of orientation estimation algorithms can be improved, therefore providing physicians with reliable objective data they can use in their daily practice.
Year
DOI
Venue
2016
10.1016/j.compbiomed.2015.08.007
Computers in Biology and Medicine
Keywords
Field
DocType
Human motion,Orientation estimation,MARG sensors,Kalman filtering,Intensity detection,Gait analysis
Computer vision,Pattern recognition,Motion intensity,Computer science,Algorithm,Kalman filter,Human motion,Gait analysis,Body posture,Artificial intelligence,Inertial measurement unit,Frequency analysis
Journal
Volume
Issue
ISSN
72
C
0010-4825
Citations 
PageRank 
References 
4
0.45
9
Authors
4
Name
Order
Citations
PageRank
alberto olivares140.45
J. M. Górriz257054.40
Javier Ramírez365668.23
gonzalo olivares440.45