Low Computational-Cost Footprint Deformities Diagnosis Sensor through Angles, Dimensions Analysis and Image Processing Techniques

Manual measurements of foot anthropometry can lead to errors since this task involves the experience of the specialist who performs them, resulting in different subjective measures from the same footprint. Moreover, some of the diagnoses that are given to classify a footprint deformity are based on a qualitative interpretation by the physician; there is no quantitative interpretation of the footprint. The importance of providing a correct and accurate diagnosis lies in the need to ensure that an appropriate treatment is provided for the improvement of the patient without risking his or her health. Therefore, this article presents a smart sensor that integrates the capture of the footprint, a low computational-cost analysis of the image and the interpretation of the results through a quantitative evaluation. The smart sensor implemented required the use of a camera (Logitech C920) connected to a Raspberry Pi 3, where a graphical interface was made for the capture and processing of the image, and it was adapted to a podoscope conventionally used by specialists such as orthopedist, physiotherapists and podiatrists. The footprint diagnosis smart sensor (FPDSS) has proven to be robust to different types of deformity, precise, sensitive and correlated in 0.99 with the measurements from the digitalized image of the ink mat.

[1]  Yu-Chi Lee,et al.  Comparing 3D foot scanning with conventional measurement methods , 2014, Journal of Foot and Ankle Research.

[2]  Chien-Hsun Tseng,et al.  Automatic footprint detection approach for the calculation of arch index and plantar pressure in a flat rubber pad , 2015, Multimedia Tools and Applications.

[3]  Ming Zhang,et al.  A 3-dimensional finite element model of the human foot and ankle for insole design. , 2005, Archives of physical medicine and rehabilitation.

[4]  Orlando K. Nakamura,et al.  Opto-electronic system for detection of flat foot by using estimation techniques: Study and approach of design , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[5]  L. Ren,et al.  Does footprint depth correlate with foot motion and pressure? , 2013, Journal of The Royal Society Interface.

[6]  A. Ghasemi,et al.  Normality Tests for Statistical Analysis: A Guide for Non-Statisticians , 2012, International journal of endocrinology and metabolism.

[7]  J. Nunley,et al.  The reliability and reproducibility of foot type measurements using a mirrored foot photo box and digital photography compared to caliper measurements. , 2007, Journal of biomechanics.

[8]  Rene de Jesus Romero-Troncoso,et al.  FPGA-based chlorophyll fluorescence measurement system with arbitrary light stimulation waveform using direct digital synthesis , 2015 .

[9]  Robin M. Queen,et al.  Describing the Medial Longitudinal Arch Using Footprint Indices and a Clinical Grading System , 2007, Foot & ankle international.

[10]  Mohamed O. Khider,et al.  A New Noninvasive Flatfoot Detector , 2015 .

[11]  Adisorn Leelasantitham,et al.  Diagnose flat foot from foot print image based on neural network , 2013, The 6th 2013 Biomedical Engineering International Conference.

[12]  Jinwook Kim,et al.  A Foot-Arch Parameter Measurement System Using a RGB-D Camera , 2017, Sensors.

[13]  Joshua Burns,et al.  Foot type and overuse injury in triathletes. , 2005, Journal of the American Podiatric Medical Association.

[14]  B. Sangeorzan,et al.  Biomechanics and pathophysiology of flat foot. , 2003, Foot and ankle clinics.

[15]  A. Delitto,et al.  Visual assessment of foot type and relationship of foot type to lower extremity injury. , 1991, The Journal of orthopaedic and sports physical therapy.

[16]  P. Cavanagh,et al.  The arch index: a useful measure from footprints. , 1987, Journal of biomechanics.

[17]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[18]  M Kouchi,et al.  Interobserver errors in anthropometry. , 1999, Journal of human ergology.

[19]  L. Staheli,et al.  The longitudinal arch. A survey of eight hundred and eighty-two feet in normal children and adults. , 1987, The Journal of bone and joint surgery. American volume.

[20]  Robin M Queen,et al.  The effect of foot type on in-shoe plantar pressure during walking and running. , 2008, Gait & posture.

[21]  T Y Shiang,et al.  Evaluating different footprint parameters as a predictor of arch height. , 1998, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[22]  Javier Bayod,et al.  Computational Foot Modeling: Scope and Applications , 2016 .

[23]  Ivan Cruz-Aceves,et al.  Fast Parabola Detection Using Estimation of Distribution Algorithms , 2017, Comput. Math. Methods Medicine.

[24]  Ryan T. Crews,et al.  Dynamic footprint measurement collection technique and intrarater reliability: ink mat, paper pedography, and electronic pedography. , 2012, Journal of the American Podiatric Medical Association.

[25]  Y. Plumarom,et al.  Comparison between Staheli index on Harris mat footprint and Talar-first metatarsal angle for the diagnosis of flatfeet. , 2014, Journal of the Medical Association of Thailand = Chotmaihet thangphaet.