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Inference System for Automatic Identification of Braille Blocks Using a Pressure Sensor Array

Visually impaired people usually use braille blocks as guidance marks when walking outside. Therefore, detecting the braille blocks accurately is very important for them to walk safely. In our previous research we proposed a novel system for recognizing braille blocks, mainly in an outdoor setting, using not only the information from a white cane but also a pressure sensor array to realize the braille block more accurately. Moreover, we showed our system could be constructed using a Neural Network with an extremely simple structure. In this research we developed an inference system for automatic identification of braille blocks based on the previous research results. The system consisted of a 16-channel foot pressure sensor array, a microcomputer and a cloud system, which respectively detected the difference in pressure pattern depending upon the type of braille block, made inferences about the kinds of braille blocks and implemented machine learning. We made a prototype system and confirmed that the system could infer the kinds of braille blocks with high accuracy in real-time.