• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.101-106
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• 2018

Most people with visual impairments can not use both hands freely because they carry a cane. And unnecessary contact with people around you during walking can occur. Other guides include guide dogs, but the price and maintenance cost are expensive and difficult to manage. In this paper, we propose a smart glove, which prevents unnecessary contact when blind people use the cane, detects the obstacle ahead by using the ultrasonic sensor, and informs the presence of the obstacle by using the servo motor. In addition, the electrostatic proximity touch module on the fingers executes a specific application of the smart phone connected by Bluetooth when the finger touches each other. It is designed to allow you to use text or phone calls when you are lost or in an emergency. The results of this study will be used as a tool to provide a more convenient life for the visually impaired.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.561-568
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• 2006

WPS(Wearable Personal Station) for next generation PC can define as a core terminal of 'Ubiquitous Computing' that include information processing and network function and overcome spatial limitation in acquisition of new information. As a way to acquire significant dynamic gesture data of user from haptic devices, traditional gesture recognizer based on desktop-PC using wire communication module has several restrictions such as conditionality on space, complexity between transmission mediums(cable elements), limitation of motion and incommodiousness on use. Accordingly, in this paper, in order to overcome these problems, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC(the embedded-ubiquitous environment using blue-tooth module and WPS). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data 2) Relational Database Management System(hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition modulo: fuzzy max-min and neural network recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy min-nin module and 96.7% in neural network recognition module about significantly dynamic gestures.