Technical Briefs

Effect of Calibration Method on Tekscan Sensor Accuracy

[+] Author and Article Information
Jill M. Brimacombe, Antony J. Hodgson

Department of Mechanical Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, BC, V6R 2L7, Canada

David R. Wilson

Division of Orthopaedic Engineering Research, Department of Orthopaedics, and Vancouver Coastal Health Research Institute, University of British Columbia, 500-828 West 10th Avenue, Vancouver, BC, V6K 1L8, Canada

Karen C. Ho

Centre for Bioengineering Research and Education, and Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada

Carolyn Anglin1

Centre for Bioengineering Research and Education, and Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canadacanglin@ucalgary.ca


Corresponding author.

J Biomech Eng 131(3), 034503 (Dec 23, 2008) (4 pages) doi:10.1115/1.3005165 History: Received October 07, 2007; Revised July 18, 2008; Published December 23, 2008

Tekscan pressure sensors are used in biomechanics research to measure joint contact loads. While the overall accuracy of these sensors has been reported previously, the effects of different calibration algorithms on sensor accuracy have not been compared. The objectives of this validation study were to determine the most appropriate calibration method supplied in the Tekscan program software and to compare its accuracy to the accuracy obtained with two user-defined calibration protocols. We evaluated the calibration accuracies for test loads within the low range, high range, and full range of the sensor. Our experimental setup used materials representing those found in standard prosthetic joints, i.e., metal against plastic. The Tekscan power calibration was the most accurate of the algorithms provided with the system software, with an overall rms error of 2.7% of the tested sensor range, whereas the linear calibrations resulted in an overall rms error of up to 24% of the tested range. The user-defined ten-point cubic calibration was almost five times more accurate, on average, than the power calibration over the full range, with an overall rms error of 0.6% of the tested range. The user-defined three-point quadratic calibration was almost twice as accurate as the Tekscan power calibration, but was sensitive to the calibration loads used. We recommend that investigators design their own calibration curves not only to improve accuracy but also to understand the range(s) of highest error and to choose the optimal points within the expected sensing range for calibration. Since output and sensor nonlinearity depend on the experimental protocol (sensor type, interface shape and materials, sensor range in use, loading method, etc.), sensor behavior should be investigated for each different application.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 1

A schematic of the three calibration methods available with the Tekscan software that were used in the current study: 20% linear, 80% linear, and power. The raw output for the sensor is the sum of all individual sensel outputs, which range from 0 to 255. The Tekscan software uses the calibration equation to convert raw sensor output to calibrated loads during testing. The raw sensor output will lead to different calibrated forces depending on the equation used. In the case shown by the arrows, the 80% calibration results in much higher calibrated loads than the 20% and power calibrations. For higher loads, the 20% calibration results in much lower calibrated loads than the other two methods.

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Figure 2

The configuration used to apply pressure to Tekscan sensors using an Instron materials testing machine

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Figure 3

Timeline of calibrations and loading. This sequence was performed for three identical sensors. For one of the sensors, three sets of experimental data were taken to test for repeatability of the results for the same calibration algorithm.

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Figure 4

Output for one sensor calibrated using the five different calibration algorithms showing overestimation of the loads when using the 80% linear calibration at lower loads and underestimation of the loads when using the 20% linear calibration at higher loads

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Figure 5

Average rms errors of Tekscan and user-defined calibration algorithms for three different testing ranges. The calibration points used for the three-point polynomial (10%, 50%, and 100%) were chosen to minimize rms errors over the full range; other selections could reduce errors in the high or low range. Error bars show the standard deviation for the three sensors tested.




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