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Author: search.filters.author.Keatsamarn, TanaponSubject: search.filters.subject.Image Processing

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    Embedded foot plantar classification system using Raspberry Pi
    (2018-05-30)
    Niemhom, Anyamanee
    ;
    Keatsamarn, Tanapon
    ;
    Pintavirooj, Chuchart
    Foot plantar classification is crucial to prevent dangers to patient's health that originated from an abnormal type of foot. When patients comprehend their type of foot, they will be able to form personal insole to prevent a hazard. On the contrary, some patients who ignore their risk will possibly have the problem. We design the system to classify type of foot by measuring foot pressure and foot curvature. For the software, we use webcams to capture the foot images that will be further processed by Raspberry Pi with OpenCV and color coding which correspond to foot pressure. For the hardware system, we use the component which is inexpensive and easily available. The hardware structure is composed of steel as a base where the transparent acrylic plate and glossy white paper is placed on. The black polypropylene sheet covering on the uppermost is used to block light from outside. Aligned on the side of the transparent acrylic plate LED strip is used for light source for the system. Underneath the steel base there are four webcams which is used to record feet images (two webcams for each foot). The images are sent to Raspberry Pi for image processing and displaying. The system is able to classify three types of the foot for patients including normal foot, high arch foot, and flat foot.
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    Foot plantar pressure measurement system using optical sensor
    (2017-02-21)
    Keatsamarn, Tanapon
    ;
    Pintavirooj, Chuchart
    Foot pressure measurement is necessary for classifying disorders of the foot and designing insole for individual person. This present work uses optical sensor (webcam) to capture the foot-pressure image. Image processing on Raspberry Pi with OpenCV library is applied to process image and color coding corresponding to foot pressure. The hardware system uses the transparent acrylic plate and uses the steel as a base of the acrylic plate. The glossy white paper is placed on the top of the transparent acrylic plate covering with polypropylene sheet on the system to block light from outside. Light in the system is released from LED strip entering from a side of the acrylic plate. The scattered light occurred in acrylic plate from the foot pressing were recorded by the webcams. The four webcams placed below facing upward for collecting images (2 cameras for each foot) and sending to Raspberry Pi. Raspberry Pi will perform image process including image enhancement and image stitching and image displaying. The result can be used to classifying foot type and find methods to prevent the occurrence of for disorders.