Technical Brief

A Force-Sensing Insole to Quantify Impact Loading to the Foot

[+] Author and Article Information
Ishan Acharya, John T. Van Tuyl

Department of Mechanical Engineering,
McMaster University,
1280 Main Street West,
Hamilton, ON L8S 4L7, Canada

Julia de Lange

School of Biomedical Engineering,
McMaster University,
1280 Main Street West,
Hamilton, ON L8S 4L7, Canada

Cheryl E. Quenneville

Department of Mechanical Engineering,
McMaster University,
1280 Main Street West,
Hamilton, ON L8S 4L7, Canada;
School of Biomedical Engineering,
McMaster University,
1280 Main Street West,
Hamilton, ON L8S 4L7, Canada
e-mail: quennev@mcmaster.ca

1Corresponding author.

Manuscript received July 15, 2017; final manuscript received October 15, 2018; published online November 29, 2018. Assoc. Editor: Beth A. Winkelstein.

J Biomech Eng 141(2), 024501 (Nov 29, 2018) (6 pages) Paper No: BIO-17-1310; doi: 10.1115/1.4041902 History: Received July 15, 2017; Revised October 15, 2018

Lower leg injuries commonly occur in frontal automobile collisions, and are associated with high disability rates. Accurate methods to predict these injuries must be developed to facilitate the testing and improvement of vehicle safety systems. Anthropomorphic test devices (ATDs) are often used to assess injury risk by mimicking the behavior of the human body in a crash while recording data from sensors at discrete locations, which are then compared to established safety limits developed by cadaveric testing. Due to the difference in compliance of cadaveric and ATD legs, the force dissipating characteristics of footwear, and the lack of direct measurement of injury risk to the foot and ankle, a novel instrumented insole was developed that could be applied equally to all specimens both during injury limit generation and during safety evaluation tests. An array of piezoresistive sensors were calibrated over a range of speeds using a pneumatic impacting apparatus, and then applied to the insole of a boot. The boot was subsequently tested and compared to loads measured using ankle and toe load cells in an ATD, and found to have an average error of 10%. The sensors also provided useful information regarding the force distribution across the sole of the foot during an impact, which may be used to develop regional injury criteria. This work has furthered the understanding of lower leg injury prediction and developed a tool that may be useful in developing accurate injury criteria in the future for the foot and lower leg.

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Grahic Jump Location
Fig. 1

Sensors and their locations on the insole of the instrumented boot. Three sensor sizes were built, and installed according to the schematic (a), resulting in an array of sensors covering the main loading regions of the insole (b). The boot was then applied to a Hybrid III ATD lower leg (c) instrumented with ankle and toe load cells (d).

Grahic Jump Location
Fig. 2

Pneumatic impacting apparatus. Impacts were applied to the sensors (or boot on an ATD lower leg) by accelerating a projectile of variable mass down a tube to strike a sponge-covered footplate, with the impact velocity and loads applied to the test specimen recorded.

Grahic Jump Location
Fig. 3

The calibration process. Each sensor was impacted at three different speeds (a). The resistance (from the sensor) versus pressure (from the load cell) was plotted for the three impacts (b). One impact was fitted with a polynomial curve (function), and the other two were fitted with this function multiplied by a scaling factor (m1 and m2, respectively). From the voltage–time curve (c), the start of impact was identified (when V dropped below 1.55 V), and the time of minimum voltage. These two time points were used to calculate the average resistance in the sensor during loading. Finally, the average resistance was plotted versus the scaling factors (m), and fitted with a final exponential function curve (d). To use the calibration during boot tests, the average resistance during any impact was calculated (using the times from the voltage curve), a corresponding m calculated, and the pressure-resistance function scaled by this m. The resistance curve from the impact test could then be converted to pressures, and finally to force via the cross-sectional area of the sensor.

Grahic Jump Location
Fig. 4

Sample pressure–resistance curves (sensor #8). Sensor resistance was measured during impacts at three speeds (6 m/s, 8 m/s, and 10 m/s). The applied pressure was determined from the axial force measurement of the in-line load cell of the apparatus divided by the sensor cross-sectional area. These data were used in the calibration process described in Fig. 3(b).

Grahic Jump Location
Fig. 5

Sample voltage curve from an impact (sensor #8, speed = 6 m/s). The voltage was monitored via the data acquisition system during the entire test. The voltage–time graph was used to isolate the impact portion of the curve, but the resistance graph was used for calibration, to eliminate any effect from source voltage changes.

Grahic Jump Location
Fig. 6

Repeatability tests. The first sensor impacted (sensor #8) was retested twice at the end of all impacts. The polynomial line of best fit equation had a lower R2 value on the retests (R2 = 0.94) than the initial test (R2 = 0.99).

Grahic Jump Location
Fig. 7

Results from boot tests. Results from the booted ATD tests for impacts: (a) 6 m/s with a 5.6 kg projectile, (b) 6 m/s with a 7.6 kg projectile, (c) 6 m/s with a 9.6 kg projectile, (d) 8 m/s with a 5.6 kg projectile, and (e) 10 m/s with a 5.6 kg projectile. Results are shown for the summed forces from all insole sensors, and compared to the sum of the compressive forces measured by the toe and ankle load cells as well as the axial forces measured at the distal and proximal load cells in the ATD tibia.



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