Talking Assistance about Location Finding both Indoor and Outdoor for Blind People

Abstract: God gifted sense to human being which is an important aspect in our life is vision. We are able to see the beauty of nature, things which happen in day-to-day life with the help of our eyes. But there are some people who lack this ability of visualizing these things. They face many difficulties to move on with their daily life. The problem gets worse when they move to an unfamiliar location. Visually impaired people face many challenges when moving in unfamiliar public places. Only few of the navigation systems for visually impaired people can provide dynamic interactions. None of these systems work perfectly both indoors and outdoors. Current navigation device for the visually impaired focus on travelling from one location to another. This focuses on designing a device for visually impaired people that help them to travelling independently also it must be comfortable to use. The proposed device is used for guiding individuals who are blind or partially sighted. The device is used to help blind people to move with the same ease and confidence as a sighted people. The device is linked with a GPS to identify the location of the blind person. Moreover, it provides the voice alert to avoid obstacles based on ultrasonic sensors. An emergency button is also added to the system. A RFID can be installed into public building and it is also integrated into blind persons walking stick .The whole device is designed to be small and is used in conjunction with the white cane

Blink talk: A machine learning-based method for women safety using EEG and eye blink signals

Women's safety is currently thought to be a big issue in both urban and rural settings. A variety of smart gadgets and software were created to provide women with security. There are a lot of smart gadgets and applications on the market, but they don't offer a good answer and are too expensive. In this research, a novel machine learning-based Blink Talk method has been proposed for women safety. EEG based on some blink talk algorithms for eye blink detection. Initially, the EEG and eye blink signals are pre-processed using Discrete wavelet transform (DWT). The pre-processed signals are fed into Stacked Denoising autoencoder (SDAE) for extracting the features. In the next phase, the extracted features are used to classify the emotions of women through Multiclass Support vector machine (MSVM). The classification results are sad, happy, normal, and fear; finally, fear emotion is converted into text using GSM to the saved contacts and nearby police station and GPS scan the near radius surrounding people to send the alert and help request message for the help needed person. The experimental results reveal that the suggested approach provides high accuracy range of 98.04%. then the traditional machine learning techniques