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

Abstract

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.

Effect of visual condition and physical activity on the plantar pressure distribution in adult and older women

“…This study applied advanced technology to analyze plantar pressure. Usually, footprint on a grid paper or podoscope is used, where measurement accuracy depends on the researcher's measurement ability, and reliability and repeatability are generally poor 10 .…”

“…There are several studies extensively discussing plantar pressure [9][10][11] . However, there is lack of studies in current literature on the relationship between visual condition, age, and physical activity and plantar pressure variations in older adults and those in the transition between adult and older ages 2,12 .…”

“…As for motor performance, it is not yet clear whether the practice of physical activity influences plantar pressure variations and foot morphology. This indicator may be important for assessing balance in this age group and as a long-term health predictor 9,10 .…”

Comparing Three Footprint Grades to Evaluate Footprint Indexes for Flat Foot Diagnosis

“…Staheli (ST) index: ST index includes two parameters and is the ratio between lines A and B. It denotes the minimum width of the midfoot and the maximum width of the hindfoot, respectively [24] (Figure 1). If the calculated number is ˃0.8, the individual has FF [25].…”

Automatic Segmentation of Monofilament Testing Sites in Plantar Images for Diabetic Foot Management

“…The GrabCut algorithm is used at a first stage to segment the background from the plantar region of interest. The foot anatomical structure was also used in [17] to support the detection of deformities from angles and dimensional analysis, using image processing. Background segmentation and plantar image binarization was also performed; however, because images were acquired using a specific device with controlled light conditions, the authors were able to use a fixed threshold value.…”