Fast Algorithm to Measure the Types of Foot Postures with Anthropometric Tests Using Image Processing
There are two types of tools for measuring the foot posture, uniplanar (anthropometric and radiographic types) and multiplanar tools (such as Foot Posture Index-6 and -8). The process of the foot posture measurement with both tools performed by a doctor were commonly carried out by using manual equipment such as ruler, arc, goniometer, marker and applying the observation skill by eyes. It needs time to measure for each foot. For research needs, a large number of samples has to be provided by a doctor to analyze data statistically which consumes much more time and exhaustion from work load in the measurement process. Hence, the aim of this study is to significantly decrease the measurement time and minimizing human error by developing a software of anthropometric measurements of foot posture based on digital image processing (DIP). The anthropometric tests used in this study consist of Rear Foot Angle (RFA), Medial Length Arc Angle (MLAA) and Arch Height Index (AHI). Instead of using equipment with a series of measurement to determine the foot posture, the DIP system only need two pictures of foot as the input of the system. The methods involved in the image processing are performed by a series of digital image processing, started from pre-image processing, noise filter, Sobel edge detection, feature extraction, calculation and classification. The result of the image processing is able to determine the foot posture types for all tests based on the values of angle and length of the foot variables. The error measurements of length and angle are 6.22 % and (0.26-1.74) %, respectively. This study has demonstrated the development algorithm in MATLAB to measure the foot posture, which is named Anthro-Posture v1.0 software. This software offers an efficient alternative way in measuring and classifying the foot posture in a shorter time and minimizing the human error in measurement process. In the future, this study can be improved to be used by doctors in obtaining large amounts of data for research needed.
Copyright (c) 2020 Husneni Mukhtar, Dien Rahmawati, Desri Kristina Silalahi
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