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

Optical-Based Foot Plantar Pressure Measurement System for Potential Application in Human Postural Control Measurement and Person Identification

Plantar pressure, the pressure exerted between the sole and the supporting surface, has great potentialities in various research fields, including footwear design, biometrics, gait analysis and the assessment of patients with diabetes. This research designs an optical-based foot plantar pressure measurement system aimed for human postural control and person identification. The proposed system consists of digital cameras installed underneath an acrylic plate covered by glossy white paper and mounted with LED strips along the side of the plate. When the light is emitted from the LED stripes, it deflects the digital cameras due to the pressure exerted between the glossy white paper and the acrylic plate. In this way, the cameras generate color-coded plantar pressure images of the subject standing on the acrylic-top platform. Our proposed system performs personal identification and postural control by extracting static and dynamic features from the generated plantar pressure images. Plantar pressure images were collected from 90 individuals (40 males, 50 females) to develop and evaluate the proposed system. In posture balance evaluation, we propose the use of a posture balance index that contains both magnitude and directional information about human posture balance control. For person identification, the experimental results show that our proposed system can achieve promising results, showing an area under the receiver operating characteristic (ROC) curve of 0.98515 (98.515%), an equal error rate (EER) of 5.8687%, and efficiency of 98.515%.

Experimental and numerical diagnosis of fatigue foot using convolutional neural network

Fatigue is an essential criterion for physiotherapy in injured athletes. Muscle fatigue mechanism also is a crucial matter in designing a workout program. It is mainly related to physical injury, cerebrovascular accident, spinal cord injury, and rheumatologic disease. The leg is one of the organs in the body where fatigue is visible, and usually, the first fatigue traces in the human body are shown. The main objective of the article is to diagnosis tired and untired feet base on digital footprint images. Therefore, the foot images of students in the age group of 20-30 were examined. The device is a digital footprint scanner. This device includes a plate screen equipped with pressure sensors and footprints in the image. A treadmill is used for 8 min to tire our test individuals. Therefore, six methods of k-nearest-neighbor classifier, multilayer perceptron, support vector machine, naïve Bayesian learning, decision tree, and convolutional neural network (CNN) architecture are presented to achieve the goal. First, the images are grayscale and divide into four regions, and the mean and variance of pressure in each of the four areas are extracted as features. Finally, the classification is accomplished using machine learning methods. Then, the results are compared with a proposed CNN architecture. The presented CNN method is outperforming other approaches and can be used for future fatigue diagnosis systems.