Low Price Foot Pressure Distribution Screening Technique: Optical Podoscope with Accurate Foot Print Segmentation using Hidden Markov Random Field Model

H Heravi, a Ebrahimi, S Nikzad, E Olyaee, Y Salek Zamani

Abstract


Background: Foot pressure assessment systems are widely used to diagnose foot pathologies. Human foot plays an important role in maintaining the biomechanical function of the lower extremities which includes provision of balance and stabilization of the body during gait.

Objective: There are different types of assessment tools with different capabilities which are discussed in detail in this paper. In this project, we introduce a new camera-based pressure distribution estimation system which can give a numerical estimation in addition to giving a visual illustration of pressure distribution of the sole.

Material and Methods: In the first step, an image is captured from the traditional Podoscope devices. Then, HMRFEM image segmentation scheme is implemented to extract the contacting part of the sole to the ground. Finally, based on a simple calibration method, per mm2 pressure is estimated to give an accurate pressure distribution measure.

Results: A significant and usable estimation of foot pressure has been introduced in this article. The main drawback of introduced systems is low resolution of sensors which is solved using a high resolution camera as a sensor. Another problem is patchy edge extracted by the systems which is automatically solved in the proposed device using an accurate image segmentation algorithm. Also the LCE, GCE and BCE measures demonstrate that lowest error rates are obtained with HMRF segmentation method.

Conclusion: we introduced a camera-based plantar pressure assessment tool which uses we introduced a camera-based plantar pressure assessment tool which uses HMRF-EM-based method has been explained in more detail which gives a brilliant sole segmentation from the captured images. Most of the marketable measurement systems use electronic sensors to estimate the pressure distribution, but here we used the captured image and grayscale levels to compute a per pixel pressure which can be converted to N/mm2 scale.


Keywords


Image Processing, Foot Print, Segmentation, Hidden Markov Random Field, Expectation Maximization

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DOI: https://doi.org/10.22086/jbpe.v0i0.618

eISSN: 2251-7200        JBPE NLM ID: 101589641

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