title: Use of weighted Fourier linear combiner filters to estimate lower trunk 3D orientation from gyroscope sensors data.
abstract: BACKGROUND
  The present study aimed at devising a data processing procedure that provides an optimal estimation of the 3-D instantaneous orientation of an inertial measurement unit (IMU). This result is usually obtained by fusing the data measured with accelerometers, gyroscopes, and magnetometers. Nevertheless, issues related to compensation of integration errors and high sensitivity of these devices to magnetic disturbances call for different solutions. In this study, a method based on the use of gyroscope data only is presented, which uses a Weighted Fourier Linear Combiner adaptive filter to perform a drift-free estimate of the 3D orientation of an IMU located on the lower trunk during walking.
  
  
  METHODS
  A tuning of the algorithm parameters and a sensitivity analysis to its initial conditions was performed using treadmill walking data from 3 healthy subjects. The accuracy of the method was then assessed using data collected from 15 young healthy subjects during treadmill walking at variable speeds and comparing the pitch, roll, and yaw angles estimated from the gyroscopes data to those obtained with a stereophotogrammetric system. Root mean square (RMS) difference and correlation coefficients (r) were used for this purpose.
  
  
  RESULTS
  An optimal set of values of the algorithm parameters was established. At all the observed speeds, and also in all the various sub-phases, the investigated angles were all estimated to within an average RMS difference of less than 1.2 deg and an average r greater than 0.90.
  
  
  CONCLUSIONS
  This study proved the effectiveness of the Weighted Fourier Linear Combiner method in accurately reconstructing the 3D orientation of an IMU located on the lower trunk of a subject during treadmill walking. This method is expected to also perform satisfactorily for overground walking data and to be applicable also to other "quasi-periodic" tasks, such as squatting, rowing, running, or swimming.
authors.affiliation: LABLAB, Department of Human Movement and Sports Sciences, University of Rome Foro Italico, Rome, Italy. bonnet.vincent@gmail.com
authors.forename: Vincent
authors.initials: V
authors.lastname: Bonnet
authors.forename: Claudia
authors.initials: C
authors.lastname: Mazzà
authors.forename: John
authors.initials: J
authors.lastname: McCamley
authors.forename: Aurelio
authors.initials: A
authors.lastname: Cappozzo
mesh_terms: D000054:Acceleration; D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena; D003627:Data Interpretation, Statistical; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D016018:Least-Squares Analysis; D008297:Male; D008875:Middle Aged; D013909:Thorax / Q000033:anatomy & histology* / Q000502:physiology*; D060726:Torso; D016138:Walking / Q000502:physiology; D055815:Young Adult