Displacement profile estimation using low cost inertial motion sensors with applications to sporting and rehabilitation exercises

Abstract

, "Displacement profile estimation using low cost inertial motion sensors with applications to sporting and rehabilitation exercises," in 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2013, pp. 1290-1295. Displacement profile estimation using low cost inertial motion sensors with applications to sporting and rehabilitation exercises Abstract This paper investigates two methods of displacement estimation using sampled acceleration and orientation data from a 6 degrees of freedom (DOF) Inertial Measurement Unit (IMU), with the application to sporting training and rehabilitation. Currently, the use of low cost IMUs for this particular application is very impractical due to the accumulation of errors from various sources. Previous studies and projects that have applied IMUs to similar applications have used a lower number of DOF, or have used higher accuracy navigational grade IMUs. Solutions to the acceleration noise accumulation and gyroscope angle error problem are proposed in this paper. A zero velocity update algorithm (ZUPT) is also developed to improve the accuracy of displacement estimation with a low grade IMU. The experimental results from this study demonstrate the feasibility of using an IMU with loose tolerances to determine the displacement. Peak distances of a range of exercises are shown to be measured with accuracies within 5% for the numerical integration methods.

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“…In this article, we used GLRT as the ZUPT method. Several ZUPT methods have been proposed before, including decision tree filtering method, Hidden Markov Model, and GLRT ( 32 – 34 ). Decision tree filtering method requires routine calibration of acceleration ( 32 ).…”

“…Several ZUPT methods have been proposed before, including decision tree filtering method, Hidden Markov Model, and GLRT ( 32 – 34 ). Decision tree filtering method requires routine calibration of acceleration ( 32 ). Hidden Markov Model uses only the gyroscope for determine the movement and cannot determine a motion with no rotation ( 33 ).…”