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Kaewrat, C.; Boonbrahm, P.; Sahoh, B. Foot-Detection Approach Based on Seven-Foot Dimensions. Encyclopedia. Available online: https://encyclopedia.pub/entry/45946 (accessed on 15 November 2024).
Kaewrat C, Boonbrahm P, Sahoh B. Foot-Detection Approach Based on Seven-Foot Dimensions. Encyclopedia. Available at: https://encyclopedia.pub/entry/45946. Accessed November 15, 2024.
Kaewrat, Charlee, Poonpong Boonbrahm, Bukhoree Sahoh. "Foot-Detection Approach Based on Seven-Foot Dimensions" Encyclopedia, https://encyclopedia.pub/entry/45946 (accessed November 15, 2024).
Kaewrat, C., Boonbrahm, P., & Sahoh, B. (2023, June 21). Foot-Detection Approach Based on Seven-Foot Dimensions. In Encyclopedia. https://encyclopedia.pub/entry/45946
Kaewrat, Charlee, et al. "Foot-Detection Approach Based on Seven-Foot Dimensions." Encyclopedia. Web. 21 June, 2023.
Foot-Detection Approach Based on Seven-Foot Dimensions
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This entry is adapted from the peer-reviewed paper 10.3390/informatics10020048

Unsuitable shoe shapes and sizes are a critical reason for unhealthy feet, may severely contribute to chronic injuries such as foot ulcers in susceptible people (e.g., diabetes patients), and thus need accurate measurements in the manner of expert-based procedures.

feet measurement foot recognition diabetic foot digital healthcare LiDAR sensor

1. Introduction

Healthy feet are the main factor for activity in daily life, whether in movement related to work or exercise [1]. Neglecting foot health may result in chronic injuries such as foot ulcers [2]. It is a significant obstacle for susceptible people who are suffered from negative long-term consequences and substantial healthcare expenses [3].
Suitable shoe shapes and sizes are essential for healthy feet and need accurate measurements. Available shoes in the commercial sectors are based on the average size, standardizing the foot base on a measuring tape (length and width). It may not be comfortable and nor fit for susceptible people such as diabetes patients [4]. Moreover, manually finding shoe sizes is tedious and time-consuming [5]. Therefore, optical measuring technologies, binocular vision, phase measurement, digital holography, and structured light techniques have become the principal machine measures for mass-customized shoes; however, detecting foot characteristics is a complex process [6]. In this way, 3D-scanning-based technology has been extensively investigated in computer graphics to address that concern [7]. It employs a laser-based technique to detect foot shape and identify size by releasing reflected light on a foot’s surface, that can then accurately compute to produce a virtual item [8].
However, accessing high-quality technologies is difficult for ordinary patients since their cost is very high, and the measurement procedure may take a very long time. Some research has attempted to overcome this limitation by proposing simple 3D-scanning devices based on RGB-depth cameras such as PrimeSense, Kinect, and RealSense. Compared to conventional 3D scanning equipment, they are affordable, portable, and simple to operate [9]. In addition, some of them apply visualization-based try-on simulations as assistive tools to help people measure foot size in order to make better decisions [10]. The visualization may utilize AR technology to manufacture relevant foot information based on smart devices. For instance, Jiang et al. proposed an AR-based approach for the virtual try-on of shoes projected on mobile devices [11]. They found that a detection system, applied to the AR technique and displayed on mobile devices, helps people effectively enhance their perception of a product.
The principals of feet detection technology consist of two main processes: (1) the information detection process and (2) the information visualization process. It may begin with a detection process based on the marker-based process that senses real-time signals from the real-world environment and extracts essential information. The results let computer systems simultaneously simulate virtual objects based on 3D models mixed with real-world objects. Technically, there are two well-known approaches for foot detection systems. Firstly, the marker-based sensing approach uses a physical symbol (such as photos or text) as a reference position using an RGB camera [12]. The current systems focus on simulating virtual foot measurements using the marker-based approach to track and monitor objects [13]. However, this technique depends on the quality of the marker positions that are placed on unmoving objects unsuitable for detecting active objects, such as the human body. It becomes a limitation when users, such as diabetes patients, need accurate foot measurements, but cannot access advanced and professional tools. Secondly, the markerless-based sensing approach utilizes the feature of a depth camera for real-time object tracking, which can also apply to foot-detection systems. Addressing this concern, by introducing a new markerless-based approach to the standard of professional tools that can be accessed through mobile devices so that ordinary people can accurately measure their shoe size at anytime and anywhere, is challenging.

2. Feet Detection

The susceptible people are those whose feet are sensitive to injury caused by unfitted footwear that may affect long-term foot health, such as chronic patients and older people [14]. Alsheikh et al. [15] researched the need for primary healthcare based on diabetic foot conditions. They found that, recently, patients have been less cared for in terms of foot health and that there is a need for further study to fill this gap. This suggests that utilizing advanced technologies is challenging but may alleviate this problem.
Researchers have addressed this concern based on machine learning techniques. D’Angelo et al. [16] proposed an approach for foot detection using interpretable machine learning techniques. They discussed how the approach could explain the problem explicitly, allowing staff to understand the foot condition and effectively decide on the treatment. Thotad et al. [17] and Ahsan et al. [18] proposed an approach for detecting diabetic foot ulcers using deep learning and computer vision techniques. They discussed the proposed techniques to help the system detect foot ulcers effectively. Some have solved the problem using sensor technologies to detect the foot shape and size. Mei [19] and Kini et al. [20] employed force sensors that measure and classify foot types and that allow the system to match each foot with proper footwear. They claimed that the techniques help people choose suitable footwear and may prevent long-term negative effects. In addition, Chun et al. [21] proposed a foot-detection approach using an RGB-D camera as a sensor. They emphasized the foot arch details critical for measuring and capturing the fit size and shape factors. They concluded that they produced high-quality inputs that effectively let the system analyze foot arch characteristics.
Although numerous studies have proposed new approaches to deal with foot measurement problems with high detection effectiveness, none consider the already-used approaches for real-time foot measuring at home with user-friendly devices. They employed high computational performance, sensors, and techniques that are not yet ready to deploy for ordinary patients who are not experts. Moreover, they focused on post-event analysis rather than on an assistive tool for patients to measure appropriate footwear shapes and sizes in order to prevent worse cases. This research gap is challenging, but it may be possible to apply lightweight sensors with real-time technologies to address this concern.

3. Foot Detection Based on Real-Time-Based System

A foot-detection technique based on a real-time system aims to estimate the shape size so that people can choose suitable shoes. It connects physical and computer-generated objects in a virtual environment, letting people envision the approximate shoes fitted to their personalities. It may employ online processing to produce a live-stream simulation of foot information in particular situations. AR offers a real-time-based system for foot detection that lets systems control the real-world environment and interact with 3D-based virtual objects; however, applying AR to automatic foot detection in medical fields is challenging [22].
Research has examined AR technologies to support real-time-based systems and has primarily been relevant to medical training [23]. Focusing on health care, Sip et al. [24] and de Assis et al. [25] proposed AR-based systems as assistive tools to help staff rehabilitate patients affected by stroke. They discussed the fact that AR technologies let physicians and experts practice their readiness for first aid. Moreover, Jeung et al. [26] employed AR technologies to guide physicians in understanding surgical processes. They confirmed that AR technologies help them reduce faults during operations effectively. This suggests that AR technologies are in demand in medical fields, and that they can help physicians and experts deal with emergency cases. Unfortunately, most of the available studies introduce AR technologies in order to support experts, and few studies concern susceptible people, such as diabetes patients, who need technologies that measure their feet, both shape and size, and which they can try at home with available devices.
In conclusion, real-time foot-detection systems for ordinary people are currently in their infant stage and demand research that needs further contribution in order to address the knowledge gaps. Researchers highlight recent research gaps in terms of two limitations: (1) a lack of study on lightweight sensors to measure foot shape based on available devices and (2) the absence of a proposed ground truth to measure and identify foot shape descriptions concerning diabetic patients. The following section will show the overview system architecture of real-time feet detection to address the challenges.

References

  1. Yang, P.; Shi, Y.; Li, S.; Tao, X.; Liu, Z.; Wang, X.; Wang, Z.L.; Chen, X. Monitoring the Degree of Comfort of Shoes In-Motion Using Triboelectric Pressure Sensors with an Ultrawide Detection Range. ACS Nano 2022, 16, 4654–4665.
  2. Yuan, M.; Li, X.; Xu, J.; Jia, C.; Li, X. 3D foot scanning using multiple cameras. Multimed. Tools Appl. 2020, 80, 22773–22793.
  3. Moore, Z.; Avsar, P.; Wilson, P.; Mairghani, M.; O’Connor, T.; Nugent, L.; Patton, D. Diabetic foot ulcers: Treatment overview and cost considerations. J. Wound Care 2021, 30, 786–791.
  4. Jurca, A.; Žabkar, J.; Džeroski, S. Analysis of 1.2 million foot scans from North America, Europe and Asia. Sci. Rep. 2019, 9, 19155.
  5. Shang, X.; Shen, Z.; Xiong, G.; Wang, F.-Y.; Liu, S.; Nyberg, T.R.; Wu, H.; Guo, C. Moving from mass customization to social manufacturing: A footwear industry case study. Int. J. Comput. Integr. Manuf. 2018, 32, 194–205.
  6. Maruyama, M.; Tabata, S.; Watanabe, Y.; Ishikawa, M. Multi-pattern Embedded Phase Shifting Using a High-Speed Projector for Fast and Accurate Dynamic 3D Measurement. In Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 12–15 March 2018.
  7. Wu, G.; Li, D.; Hu, P.; Zhong, Y.; Pan, N. Automatic foot scanning and measurement based on multiple -depth cameras. Text. Res. J. 2016, 88, 167–181.
  8. Banwell, H.A.; Paris, M.E.; Mackintosh, S.; Williams, C.M. Paediatric flexible flat foot: How are we measuring it and are we getting it right? A systematic review. J. Foot Ankle Res. 2018, 11, 21.
  9. Guo, K.; Xu, F.; Yu, T.; Liu, X.; Dai, Q.; Liu, Y. Real-time geometry, albedo and motion reconstruction using a single camera. ACM Trans. Graph. 2017, 36, 1.
  10. Paulo, M.M.; Rita, P.; Oliveira, T.; Moro, S. Understanding mobile augmented reality adoption in a consumer context. J. Hosp. Tour. Technol. 2018, 9, 142–157.
  11. Jiang, Q.; Sun, J.; Yang, C.; Gu, C. The Impact of Perceived Interactivity and Intrinsic Value on Users’ Continuance Intention in Using Mobile Augmented Reality Virtual Shoe-Try-On Function. Systems 2021, 10, 3.
  12. Alkhamisi, A.O.; Monowar, M.M. Rise of Augmented Reality: Current and Future Application Areas. Int. J. Internet Distrib. Syst. 2013, 01, 25–34.
  13. Pachoulakis, I. Augmented Reality Platforms for Virtual Fitting Rooms. Int. J. Multimed. Appl. 2012, 4, 35–46.
  14. Saghazadeh, M.; Kitano, N.; Okura, T. Gender differences of foot characteristics in older Japanese adults using a 3D foot scanner. J. Foot Ankle Res. 2015, 8, 29.
  15. Alsheikh, S.; AlGhofili, H.; Alageel, R.; Ababtain, O.; Alarify, G.; Alwehaibi, N.; Altoijry, A. Diabetic Foot Care: A Screening on Primary Care Providers’ Attitude and Practice in Riyadh, Saudi Arabia. Medicina 2022, 59, 64.
  16. D’Angelo, G.; Della-Morte, D.; Pastore, D.; Donadel, G.; De Stefano, A.; Palmieri, F. Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach. Future Gener. Comput. Syst. 2023, 140, 138–150.
  17. Thotad, P.N.; Bharamagoudar, G.R.; Anami, B.S. Diabetic foot ulcer detection using deep learning approaches. Sens. Int. 2023, 4, 100210.
  18. Ahsan, M.; Naz, S.; Ahmad, R.; Ehsan, H.; Sikandar, A. A Deep Learning Approach for Diabetic Foot Ulcer Classification and Recognition. Information 2023, 14, 36.
  19. Mei, Z.; Ivanov, K.; Zhao, G.; Wu, Y.; Liu, M.; Wang, L. Foot type classification using sensor-enabled footwear and 1D-CNN. Measurement 2020, 165, 108184.
  20. Kini, K.R.; Madakyaru, M.; Harrou, F.; Sun, Y. Detecting pediatric foot deformities using plantar pressure measurements: A semi-supervised approach. IEEE Des. Test 2023, 1-1.
  21. Chun, S.; Kong, S.; Mun, K.R.; Kim, J. A Foot-Arch Parameter Measurement System Using a RGB-D Camera. Sensors 2017, 17, 1796.
  22. Kim, J.H.; Kim, M.; Park, M.; Yoo, J. Immersive interactive technologies and virtual shopping experiences: Differences in consumer perceptions between augmented reality (AR) and virtual reality (VR). Telemat. Inform. 2023, 77, 101936.
  23. Serrano-Vergel, R.; Morillo, P.; Casas-Yrurzum, S.; Cruz-Neira, C. Exploring the Suitability of Using Virtual Reality and Augmented Reality for Anatomy Training. IEEE Trans. Hum.-Mach. Syst. 2023, 53, 378–389.
  24. Sip, P.; Kozłowska, M.; Czysz, D.; Daroszewski, P.; Lisiński, P. Perspectives of Motor Functional Upper Extremity Recovery with the Use of Immersive Virtual Reality in Stroke Patients. Sensors 2023, 23, 712.
  25. De Assis, G.A.; Brandão, A.F.; Correa, A.G.D.; Castellano, G. Characterization of Functional Connectivity in Chronic Stroke Subjects after Augmented Reality Training. Virtual Worlds 2023, 2, 1.
  26. Jeung, D.; Jung, K.; Lee, H.J.; Hong, J. Augmented reality-based surgical guidance for wrist arthroscopy with bone-shift compensation. Comput. Methods Programs Biomed. 2023, 230, 107323.
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Subjects: Computer Science, Information Systems; Medical Informatics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Charlee Kaewrat
,
Poonpong Boonbrahm
,
Bukhoree Sahoh
View Times: 418
Revisions: 2 times (View History)
Update Date: 25 Jun 2023
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