An Extended Kalman Filter to Estimate Human Gait Parameters and Walking Distance
Terrell R. Bennett, Roozbeh Jafari, Nicholas Gans

Citation
Terrell R. Bennett, Roozbeh Jafari, Nicholas Gans. "An Extended Kalman Filter to Estimate Human Gait Parameters and Walking Distance". Talk or presentation, 5, November, 2013; Poster presented at the 2013 TerraSwarm Annual Meeting.

Abstract
In this work, we present a novel method to estimate joint angles and distance traveled by a human while walking. We model the human leg as a two-link revolute robot. Inertial measurement sensors placed on the thigh and shin provide the required measurement inputs. The model and inputs are then used to estimate the desired state parameters associated with forward motion using an extended Kalman ļ¬lter (EKF). Experimental results with subjects walking in a straight line show that distance walked can be measured with accuracy comparable to a state of the art motion tracking systems. The EKF had an average RMSE of 7 cm over the trials with an average accuracy of greater than 97% for linear displacement.

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  • HTML
    Terrell R. Bennett, Roozbeh Jafari, Nicholas Gans. <a
    href="http://www.terraswarm.org/pubs/208.html"><i>An
    Extended Kalman Filter to Estimate Human Gait Parameters and
    Walking Distance</i></a>, Talk or presentation, 
    5, November, 2013; Poster presented at the <a
    href="http://www.terraswarm.org/conferences/13/annual"
    >2013 TerraSwarm Annual Meeting</a>.
  • Plain text
    Terrell R. Bennett, Roozbeh Jafari, Nicholas Gans. "An
    Extended Kalman Filter to Estimate Human Gait Parameters and
    Walking Distance". Talk or presentation,  5, November,
    2013; Poster presented at the <a
    href="http://www.terraswarm.org/conferences/13/annual"
    >2013 TerraSwarm Annual Meeting</a>.
  • BibTeX
    @presentation{BennettJafariGans13_ExtendedKalmanFilterToEstimateHumanGaitParametersWalking,
        author = {Terrell R. Bennett and Roozbeh Jafari and Nicholas
                  Gans},
        title = {An Extended Kalman Filter to Estimate Human Gait
                  Parameters and Walking Distance},
        day = {5},
        month = {November},
        year = {2013},
        note = {Poster presented at the <a
                  href="http://www.terraswarm.org/conferences/13/annual"
                  >2013 TerraSwarm Annual Meeting</a>.},
        abstract = {In this work, we present a novel method to
                  estimate joint angles and distance traveled by a
                  human while walking. We model the human leg as a
                  two-link revolute robot. Inertial measurement
                  sensors placed on the thigh and shin provide the
                  required measurement inputs. The model and inputs
                  are then used to estimate the desired state
                  parameters associated with forward motion using an
                  extended Kalman filter (EKF). Experimental
                  results with subjects walking in a straight line
                  show that distance walked can be measured with
                  accuracy comparable to a state of the art motion
                  tracking systems. The EKF had an average RMSE of 7
                  cm over the trials with an average accuracy of
                  greater than 97% for linear displacement. },
        URL = {http://terraswarm.org/pubs/208.html}
    }
    

Posted by Terrell R. Bennett on 13 Nov 2013.
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