Abstract
The foot is a vital organ, as it stabilizes the impact forces between
the human skeletal system and the ground. Hence, precise foot dimensions
are essential not only for custom footwear design, but also for the
clinical treatment of foot health. Most existing research on measuring
foot dimensions depends on a heavy setup environment, which is costly
and ineffective for daily use. In addition, there are several smartphone
applications online, but they are not suitable for measuring the exact
foot shape for custom footwear, both in clinical practice and public
use. In this study, we designed and implemented computer-vision-based
smartphone application OptiFit that
provides the functionality to automatically measure the four essential
dimensions (length, width, arch height, and instep girth) of a human
foot from images and 3D scans. We present an instep girth measurement
algorithm, and we used a pixel per metric algorithm for measurement;
these algorithms were accordingly integrated with the application.
Afterwards, we evaluated our application using 19 medical-grade silicon
foot models (12 males and 7 females) from different age groups. Our
experimental evaluation shows that OptiFit
could measure the length, width, arch height, and instep girth with an
accuracy of 95.23%, 96.54%, 89.14%, and 99.52%, respectively. A
two-tailed paired t-test was conducted,
and only the instep girth dimension showed a significant discrepancy
between the manual measurement (MM) and the application-based
measurement (AM). We developed a linear regression model to adjust the
error. Further, we performed comparative analysis demonstrating that
there were no significant errors between MM and AM, and the application
offers satisfactory performance as a foot-measuring application. Unlike
other applications, the iOS application we developed, OptiFit,
fulfils the requirements to automatically measure the exact foot
dimensions for individually fitted footwear. Therefore, the application
can facilitate proper foot measurement and enhance awareness to prevent
foot-related problems caused by inappropriate footwear.
Original language | English |
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Article number | 9554 |
Number of pages | 17 |
Journal | Sensors (Switzerland) |
Volume | 22 |
Issue number | 23 |
DOIs | |
Publication status | Published - 06 Dec 2022 |
Fingerprint
Dive into the research topics of 'OptiFit: Computer-vision-based Smartphone Application to measure the foot from images and 3D scans'. Together they form a unique fingerprint.Prizes
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Grant: Investigating the viability of developing a digital foot measurement app using smartphones
Kabir, A. (Recipient), 2020
Prize: Grant › Successful
Impacts
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Revolutionizing Footcare: Innovative Foot and Leg Scanning Tool Enhances Service Delivery
Kabir, A. (Creator)
Impact: Other Impact
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