Smartphone based foot scanners

NewsBot · Aug 14, 2021

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Foot-surface-structure analysis using a smartphone-based 3D foot scanner
TomokoYamashita et al
Medical Engineering & Physics; 10 August 2021

Highlights
•A 3D foot-scanning method to analyze foot features is proposed
•The second toe–heel–navicular (SHN) angle and the axis of the bone distance (ABD) are dominant midfoot skeletal features
•The transverse arch height (TAH) and the transverse arch length (TAL) are dominant forefoot-related features
•Age-dependent variations in male and female foot features are revealed
•The results obtained are a significant step toward HV prevention
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Background
A thorough understanding of the influence of the foot skeletal structure on hallux valgus (HV) is required for HV prevention. We developed a system using a 3D foot scanner on a smartphone to clarify the relationships between foot features and HV risk.

Methods
Two-dimensional video images were recorded on a smartphone, sent to a computer or cloud server, and used to construct a 3D foot-feature model, considering 10 foot features associated with HV. The participants (419 individuals, aged 40–89 years) stood with their toes 12 cm apart and heels 8 cm apart during video recording. The height and weight were measured for body-mass index calculation.

Results
Age-dependent foot-feature variations were observed slightly for males and distinctively for females. For females, the great toe–first metatarsal head–heel (GFH) angle associated with HV increased with age, i.e., the GFH angle increased with age, suggesting that HV increased with age. Multiple regression analysis revealed that the features determining the GFH angle are the second toe–heel–navicular angle, bone distance axis, and transverse arch length and height. The adjusted coefficients of determination were 0.54 and 0.52 for males and females, respectively.

Conclusion
This approach enables simple foot structure assessment for HV risk evaluation.
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NewsBot · Aug 14, 2021

Related:
Smartphones for Gait Analysis

NewsBot · Sep 4, 2021

Foot-surface-structure analysis using a smartphone-based 3D foot scanner
Tomoko Yamashita et al
Med Eng Phys. 2021 Sep;95:90-96

Background: A thorough understanding of the influence of the foot skeletal structure on hallux valgus (HV) is required for HV prevention. We developed a system using a 3D foot scanner on a smartphone to clarify the relationships between foot features and HV risk.

Methods: Two-dimensional video images were recorded on a smartphone, sent to a computer or cloud server, and used to construct a 3D foot-feature model, considering 10 foot features associated with HV. The participants (419 individuals, aged 40-89 years) stood with their toes 12 cm apart and heels 8 cm apart during video recording. The height and weight were measured for body-mass index calculation.

Results: Age-dependent foot-feature variations were observed slightly for males and distinctively for females. For females, the great toe-first metatarsal head-heel (GFH) angle associated with HV increased with age, i.e., the GFH angle increased with age, suggesting that HV increased with age. Multiple regression analysis revealed that the features determining the GFH angle are the second toe-heel-navicular angle, bone distance axis, and transverse arch length and height. The adjusted coefficients of determination were 0.54 and 0.52 for males and females, respectively.

Conclusion: This approach enables simple foot structure assessment for HV risk evaluation.
Click to expand...

NewsBot · Nov 3, 2021

The reliability of a smartphone application in measuring the foot structure of cyclists during sitting and standing
Cristina San Emeterio,Héctor Menéndez,Paloma Guillén-Rogel &Pedro J. Marín
Footwear Science: 01 Nov 2021

Previous studies have focussed on the relation between foot structure and function, revealing that 3D scanning is the measurement tool with higher precision and accuracy. However, to date only non-studies have investigated the test–retest reliability and potential of a smartphone 3D application for measuring foot morphology. The purpose of the present study was to assess the intra-rater reliability of the Avatar3D application in analysing the foot structure of cyclists. Thirty-five female road cyclists assessed her own foot through the Avatar3D application on two occasions separated by 10 minutes. Instep girth, foot length, ball girth, and ball width were registered in sitting and standing positions during the test and retest. The reliability of the measurements was evaluated using intraclass correlation coefficient (ICC), coefficient of variation (CV’s) and Bland–Altman plots. All dependent variables showed ICC range values from moderate to very high with low CV’s (<6%), evidencing no significant changes in any variable. This thesis was reinforced by Bland–Altman plots and one sample t-tests (p > 0.05). The Avatar3D application is a reliable and easy-to-use tool for assessing the foot structure in female cyclists in sitting and standing positions.
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NewsBot · Dec 11, 2022

OptiFit: Computer-Vision-Based Smartphone Application to Measure the Foot from Images and 3D Scans
Riyad Bin Rafiq et al
Sensors 2022, 22(23), 9554; https://doi.org/10.3390/s22239554

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.
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Smartphone based foot scanners

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Smartphone based foot scanners

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

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