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Research Papers

Use of Gait-Kinematics in Sensor-Based Gait Monitoring: A Feasibility Study

[+] Author and Article Information
Demoz Gebre-Egziabher, Ryan Kirker

Department of Aerospace
Engineering and Mechanics,
University of Minnesota,
Minneapolis, MN 55455

Michael Schwartz

Department of Orthopedic Surgery,
University of Minnesota,
Minneapolis, MN 55455

Manuscript received June 19, 2012; final manuscript received May 30, 2013; accepted manuscript posted June 5, 2013; published online September 23, 2013. Assoc. Editor: Martin Ostoja-Starzewski.

J. Appl. Mech 81(4), 041002 (Sep 23, 2013) (11 pages) Paper No: JAM-12-1247; doi: 10.1115/1.4024771 History: Received June 19, 2012; Revised May 30, 2013; Accepted June 05, 2013

A concept for fusing information from the kinematics describing human locomotion with body-fixed sensors for the purpose of in situ gait monitoring is studied. This is done by using an individual's gait patterns (as captured by a simplified kinematic model) with acceleration measurements made at key points on the body. The gait patterns are expressed as nominal relations between shank, thigh, and stance leg angles during normal walking. It is shown how the use of known gait patterns reduces the required number of sensors attached to the body that are required for a sensor-based monitoring of gait. The feasibility of the approach is demonstrated using a single acceleration measurement at the ankle to estimate limb angles and step size in situ. Such gait monitoring may be used for the evaluations of a subject's overall quality of gait through the determination of flexions at the knees and hip. In addition, step sizes, distance walked, and speed can be estimated. Apart from gait analysis, the method can be used for remotely monitoring the safety of individuals to the extent this can be done through consideration of the state of gait.

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Copyright © 2014 by ASME
Topics: Sensors , Errors , Knee , Kinematics
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Figures

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Fig. 1

A basic rigid body model for gait

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Fig. 2

Definition of measured accelerations in the sagittal plane

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Fig. 3

Thigh angle versus shank angle during the swing phase of steps for (a) Subject 1, and for (b) Subject 2

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Fig. 4

Stance angle versus shank angle during the swing phase of steps for (a) Subject 1 and for (b) Subject 2

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Fig. 5

Actual (solid) and calculated (dashed) steps: shank, thigh, and stance leg angles are shown for the swing phase of three steps from Set 1

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Fig. 8

Evolution of the components of the covariance matrix Pk plotted with respect to time for steps of Set 1

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Fig. 9

Step size determination error frequencies for steps of Set 1

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Fig. 10

Step size determination error frequencies for steps of Set 2

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Fig. 6

Frequency plots for mean angle determination error and corresponding standard deviations for steps of Set 1

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Fig. 7

Frequency plots for mean angle determination errors and corresponding standard deviations for steps of Set 2

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Fig. 11

Hip and knee angle error frequencies for steps of Set 1

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Fig. 12

Hip and knee angle error frequencies for steps of Set 2

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